{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-1 Illustate 2D convolution of Images through a Toy example¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scipy version: 1.7.0\n",
      "numpy version: 1.20.2\n",
      "\n",
      "\n",
      "Scipy convolve2d output\n",
      "\n",
      "[[ -2  -8  -7  -6  -5  -4  28]\n",
      " [  5  -3  -4  -5  -6  -7  28]\n",
      " [ -2 -10 -11 -12 -13 -14  28]\n",
      " [ -9 -17 -18 -19 -20 -21  28]\n",
      " [-16 -24 -25 -26 -27 -28  28]\n",
      " [-23 -31 -32 -33 -34 -35  28]\n",
      " [-29  13  13  13  13  13  27]]\n",
      "\n",
      "\n",
      "2D convolution implementation\n",
      "\n",
      "[[ -2.  -8.  -7.  -6.  -5.  -4.  28.]\n",
      " [  5.  -3.  -4.  -5.  -6.  -7.  28.]\n",
      " [ -2. -10. -11. -12. -13. -14.  28.]\n",
      " [ -9. -17. -18. -19. -20. -21.  28.]\n",
      " [-16. -24. -25. -26. -27. -28.  28.]\n",
      " [-23. -31. -32. -33. -34. -35.  28.]\n",
      " [-29.  13.  13.  13.  13.  13.  27.]]\n"
     ]
    }
   ],
   "source": [
    "## Illustate 2D convolution of Images through a Toy example\n",
    "import scipy\n",
    "import scipy.signal\n",
    "import numpy as np\n",
    "print(f\"scipy version: {scipy.__version__}\")\n",
    "print(f\"numpy version: {np.__version__}\")\n",
    "print('\\n')\n",
    "\n",
    "# Take a 7x7 image as example\n",
    "\n",
    "image = np.array([[1, 2, 3, 4, 5, 6, 7],\n",
    "                 [8, 9, 10, 11, 12, 13, 14],\n",
    "                 [15, 16, 17, 18, 19, 20, 21],\n",
    "                 [22, 23, 24, 25, 26, 27, 28],\n",
    "                 [29, 30, 31, 32, 33, 34, 35],\n",
    "                 [36, 37, 38, 39, 40, 41, 42],\n",
    "                 [43, 44, 45, 46, 47, 48, 49]])\n",
    "\n",
    "# Defined an image processing kernel\n",
    "\n",
    "filter_kernel = np.array([[-1, 1, -1],\n",
    "                          [-2, 3, 1],\n",
    "                          [2, -4, 0]])\n",
    "\n",
    "# Convolve the image with the filter kernel through scipy 2D convolution to produce an output image of same dimension as that of the input\n",
    " \n",
    "I = scipy.signal.convolve2d(image, filter_kernel,mode='same', boundary='fill', fillvalue=0)\n",
    "print(f'Scipy convolve2d output\\n')\n",
    "print(I)\n",
    "print('\\n')\n",
    "\n",
    "# We replicate the same logic of a Scipy 2D convolution by following the below steps\n",
    "# a) The boundaries need to be extended in both directions for the image and padded with zeroes.\n",
    "#    For convolving the 7x7 image by 3x3 kernel the dimensions needs to be extended by (3-1)/2 i.e 1\n",
    "#    on either size for each dimension. So a skeleton image of 9x9 image would be created\n",
    "#    in which the boundaries of 1 pixel are pre-filled with zero.\n",
    "# b) The kernel needs to be flipped i.e rotated by 180 degrees\n",
    "# c) The flipped kernel needs to placed at each cordinate location for the image and then the sum of\n",
    "#    cordinatewise product with the image intensities need to be computed. These sum for each co-ordinate would give\n",
    "#    the intensities for the output image.\n",
    "\n",
    "row,col=7,7\n",
    "\n",
    "## Rotate the filter kernel twice by 90 degree to get 180 rotation\n",
    "\n",
    "filter_kernel_flipped = np.rot90(filter_kernel,2)\n",
    "\n",
    "## Pad the boundaries of the image with zeroes and fill the rest from the original image\n",
    "\n",
    "image1 = np.zeros((9,9))\n",
    "\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        image1[i+1,j+1] = image[i,j]\n",
    "\n",
    "#print(image1)\n",
    "\n",
    "## Define the output image\n",
    "\n",
    "image_out = np.zeros((row,col))\n",
    "\n",
    "## Dynamic shifting of the flipped filter at each image cordinate and then computing the convolved sum.\n",
    "\n",
    "for i in range(1,1+row):\n",
    "    for j in range(1,1+col):\n",
    "        arr_chunk = np.zeros((3,3))\n",
    "        for k,k1 in zip(range(i-1,i+2),range(3)):\n",
    "            for l,l1 in zip(range(j-1,j+2),range(3)):\n",
    "                arr_chunk[k1,l1] = image1[k,l]\n",
    "\n",
    "        image_out[i-1,j-1] = np.sum(np.multiply(arr_chunk,filter_kernel_flipped))\n",
    "print(f\"2D convolution implementation\\n\")\n",
    "print(image_out) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-2 Convolution of an Image with Mean filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opencv version 4.6.0\n",
      "Image after applying Gaussian Noise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f50a7bbd580>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "print(\"Opencv version\",cv2.__version__)\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import convolve2d\n",
    "%matplotlib inline\n",
    "\n",
    "img = cv2.imread('monalisa.jpeg')\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "plt.imshow(gray,cmap='gray')\n",
    "mean = 0\n",
    "var = 100\n",
    "sigma = var**0.5\n",
    "row,col = np.shape(gray)\n",
    "gauss = np.random.normal(mean,sigma,(row,col))\n",
    "gauss = gauss.reshape(row,col)\n",
    "gray_noisy = gray + gauss\n",
    "print(f\"Image after applying Gaussian Noise\")\n",
    "plt.imshow(gray_noisy,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after convolving with Mean filter\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5057803e80>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Mean filter\n",
    "Hm = np.array([[1,1,1],[1,1,1],[1,1,1]])/float(9)\n",
    "Gm = convolve2d(gray_noisy,Hm,mode='same')\n",
    "print(f\"Image after convolving with Mean filter\\n\")\n",
    "plt.imshow(Gm,cmap='gray') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-3. Median Filter illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after applying Salt and Pepper Noise\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f50577fd790>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#----------------------------------------------------------------------------------------\n",
    "## First create an image with Salt and Pepper Noise \n",
    "#----------------------------------------------------------------------------------------\n",
    "## Generate random integers from 0 to 20\n",
    "## If the value is zero we will replace the image pixel with a low value of 0 that corresponds to a black pixel\n",
    "## If the value is 20 we will replace the image pixel with a high value of 255 that correspondsa to a white pixel\n",
    "## Since we have taken 20 intergers and out of which we will only tag integers 1 and 20 as salt and pepper noise\n",
    "## hence we would have approximately 10% of the overall pixels as salt and pepper noise. If we want to reduce it\n",
    "## to 5 % we can taken integers from 0 to 40 and then treat 0 as indicator for black pixel and 40 as an indicator for white pixel.\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "gray_sp = gray*1\n",
    "sp_indices = np.random.randint(0,21,[row,col])\n",
    "\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        if sp_indices[i,j] == 0:\n",
    "            gray_sp[i,j] = 0\n",
    "        if sp_indices[i,j] == 20:\n",
    "            gray_sp[i,j] = 255\n",
    "print(f\"Image after applying Salt and Pepper Noise\\n\")\n",
    "plt.imshow(gray_sp,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing Salt and Pepper Noise with OpenCV Median Filter\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055f68430>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-----------------------------------------------------------------------------------------------------------\n",
    "# Remove the Salt and Pepper Noise \n",
    "#-----------------------------------------------------------------------------------------------------------\n",
    "## Now we want to remove the salt and pepper noise through a median filter.\n",
    "## Using the opencv Median Filter for the same\n",
    "\n",
    "gray_sp_removed = cv2.medianBlur(gray_sp,3)\n",
    "print(f\"Removing Salt and Pepper Noise with OpenCV Median Filter\\n\")\n",
    "plt.imshow(gray_sp_removed,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image produced by applying Median Filter Logic\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055ed0cd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Implementation of the 3x3 Median Filter without using opencv\n",
    "\n",
    "gray_sp_removed_exp = gray*1\n",
    "\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        local_arr = []\n",
    "        for k in range(np.max([0,i-1]),np.min([i+2,row])):\n",
    "            for l in range(np.max([0,j-1]),np.min([j+2,col])):\n",
    "                local_arr.append(gray_sp[k,l])\n",
    "\n",
    "        gray_sp_removed_exp[i,j] = np.median(local_arr)\n",
    "print(f\"Image produced by applying Median Filter Logic\\n\")       \n",
    "plt.imshow(gray_sp_removed_exp,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-4. Illustration of the Guassian Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Blur Filter\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055eb5ee0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the Gaussian Filter \n",
    "Hg = np.zeros((20,20))\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        Hg[i,j] = np.exp(-((i-10)**2 + (j-10)**2)/10)\n",
    "print(f\"Gaussian Blur Filter\\n\")\n",
    "plt.imshow(Hg,cmap='gray')\n",
    "\n",
    "gray_blur = convolve2d(gray,Hg,mode='same')\n",
    "print(f\"Image after convolving with  Gaussiab Blur Filter Created above\\n\")\n",
    "plt.imshow(gray_blur,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after convolving with  Gaussiab Blur Filter Created above\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055e1c040>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gray_blur = convolve2d(gray,Hg,mode='same')\n",
    "print(f\"Image after convolving with  Gaussiab Blur Filter Created above\\n\")\n",
    "plt.imshow(gray_blur,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High Frequency Component of Image\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055d87760>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gray_high = gray - gray_blur\n",
    "print(f\"High Frequency Component of Image\\n\")\n",
    "plt.imshow(gray_high,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enhanced Image with some portion of High Frequency Component added\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055d74220>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gray_enhanced = gray + 0.025*gray_high\n",
    "print(f\"Enhanced Image with some portion of High Frequency Component added\\n\")\n",
    "plt.imshow(gray_enhanced,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-5 Convolution using a Sobel Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after convolving with Horizontal Sobel Filter\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055cd2dc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Hx = np.array([[ 1,0, -1],[2,0,-2],[1,0,-1]],dtype=np.float32)\n",
    "Gx = convolve2d(gray,Hx,mode='same')\n",
    "print(f\"Image after convolving with Horizontal Sobel Filter\\n\")\n",
    "plt.imshow(Gx,cmap='gray')\n",
    "\n",
    "Hy = np.array([[ -1,-2, -1],[0,0,0],[1,2,1]],dtype=np.float32)\n",
    "Gy = convolve2d(gray,Hy,mode='same')\n",
    "print(f\"Image after convolving with Vertical Sobel Filter\\n\")\n",
    "plt.imshow(Gy,cmap='gray')\n",
    "\n",
    "G = (Gx*Gx + Gy*Gy)**0.5\n",
    "print(f'Image after combining outputs from both Horizontal and Vertical Sobel Filters')\n",
    "plt.imshow(G,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after convolving with Vertical Sobel Filter\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055c3e790>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALkAAAD8CAYAAAArOAWDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABuwUlEQVR4nO29a4htW3Ye9s29a79f9a5z6pzT59x7+8VtCSltoTbYGAcTxxKBjv4IKRBLjrD9Q8IEEnDb/hGRIFCCH1gkmMhERAqx2wJHqDGdyJJAmIDVlrqttvqR7r73dvc9z3rXfr/3yo9d36xvjZpr7V3ncc8+lzOgqKq915prrjnHHPMb3xhzThdFEV7La/kwS+ZlV+C1vJYXLa+V/LV86OW1kr+WD728VvLX8qGX10r+Wj708lrJX8uHXl6Ykjvn/opz7lvOuXecc597Uc95La9lkbgXwZM757IAvg3gPwHwAMAfAfjpKIq+8dwf9lpeywJ5UZb8RwG8E0XRe1EUjQB8HsBnX9CzXstrSZW1F1TuLQD35f8HAD6TdHGxWIyKxSKiKMKyM0sURXDOxf5fVvS+0OdalnPuWmUnCcvgb+fcwnrw+tlstvAeW++0z1Rms9mVtlzmWcs+X8u7juj1SX9rW2azWZydnR1HUbRjy3pRSr5QnHN/A8DfAIBarYaf/dmfRT6fRyaTwWg0wng8BgBkMlcnm0wmE1NIdtRsNsNkMvFKcfEcOOewtraGtbU1P5Bms1nsOpbJ39rJvNbes6xofUPf6bMBYDqdxt6Jz8xkMshms/5HJYoiDIdDDIfDK8rG90979rISRREmk0nq91am02nwHpZ1HSOSzWaRy+Wu9Fcul8Ov/MqvfD90z4tS8ocA7sj/ty8+8xJF0a8C+FUA2Nvbi6igAGIdnGSdstlsTAmoCLlc7ooVYhlWWaMownQ6xXQ69ddns9kriresYlP5QkqTpkiZTAZra2vIZDKIosgP9Nlshnw+j0KhgCiK/OBX626fkc/ng58nKTMHjpUkxZvNZr7t7TPW1taCZbGNlxG292QySRxMnHm0n3K5XGKZL0rJ/wjAx5xzb2Cu3D8F4L9IupgNxIbLZDIoFouJSs7pCYhbPeccisVi0PpPp1OMx+Mrik4F48BgwwHwswKVl9cCYSVQaxz6ziqZKiufyzoVi0Wsra35egNAPp9HsViMzXQq+Xzev4utY5KxmM1mQQVM+pyWlM/QZyVZ+TQjoUaBdVxbW4s9Q0WVnwNU9SEkL0TJoyiaOOd+AcDvAMgC+LUoir6edD07lVYql8shl8vFGlo7SRWC1+noDk3lwLxB7Syh079ahul0ina7jel0imw2i0KhgFwu58tOmpb5DgntEryH70lLzr+dc1cUhJbeviM/00HGa1h+6Pkh6BOq66J3UhhoRQ2HvTfNwtt71DfRMhbBrReGyaMo+iKALy57vSqYWlZrMXTk87deb51XVfCk8nK5nG9wtay1Ws2Xk1Qf28BJymQdTnYgFZs/CpGGwyHy+TzK5TIAYDweYzKZIJvNolgsBttQlYx/c2CoaL2T/J4Q9JpOpxiNRkG4ou9lyw/Bx5BvpHW3nysJkNQfIXlpjqeKWi8qrFpEvqwqqipzJpPxzgjxXxJTo5BELd9kMsFgMPCQJpfLoV6vI5fL+Y6dTCbe8rDj1JJkMhkUCoXgO9r7+JtWlAoMwEM3vQeYwxE656FO5ezEH95PwxFSwKR24iALiTU0bIeQM6/14t8qFsdbvK3103ejvDJKbpWDECGp4mxQHcn04ENWBgg3AhuSA6tQKMQs5Gg08h2tFoT/L8sKWKyuCkjF1rI44EO4NA0W8Fk6SwHwjlxI0hzlECPD70KfZbPZ2D1UWGuQVPg/r9X2TfIJQmWk4f6VUHIAwZezI5xiMThfMs3LThvpZFcsC6HYXafKZd5DRTs5VE5owNhBpZ8n1cleax26JJ8gBFeuyxCF6p12fZJlTzNQ+s5a9iL2a2WU3I5k/ZxC5kG/U4ueZn2ShFAEuLTsal1CCnIdXtfKMgquWDokIfYk7VkKrRbVRz8P9UVSOWn9tuyst4zDrn5T2vNVVkbJl+m4NOeJ94aotaTrgauUlDrASZbR1lOtyzLWK8na87tsNhvzK/R6fq9Y2H5vy0wL+IQ47LQZIYnNCL1/yDkN3Z82I9kZMIrCNGUSdQuskJKrqDVeNG3aaYvcdmjQJDWupdA4YyQp7aIBQLHPpiO1qDyyIXyPNMuZ1Bb2/yQKz/oLae+zjJO3qIwkZUyLL+jf7JvrRJ5XRslpvSaTCdbW1rynTu+bnU38zKgbLRHDw+rJWyysGF6dTmUB9IcMj+1c5ZZDz9B6sb6FQiHINqgTGaISbYfqbMXnafvo5/qj5WvEl6LXWWfRvn8Id+vz7W9LzdJ5tM/RuljfiD9JrMsrAVeAS0fTOpPA1ak+RFWpJOFc+z9551DHMnik5asi62f2ucr58xobzEjqNNYvKefDWn6lGtWf4G+rLLbeabPCsg5oEnwD4oyIVeAkSfKHkgbVK6PkQBw/2g4DLl/OJmGlRSJZlloyYjvnHCaTSRDvpzmySU6cYlOl55KCRPos+9kiZ9diZA7INDZlmWfbZ9jr9be9LglahRQ7ySFNq3dIsRcNmJVQ8iSHMQkv8p7rfK7TYyijMITxQolIaaKDj/eqFSVdp1N3kqytrQUDS2wra+FDBmEZsVZ3GcYm5Cck+Q7LShpzk3TtsrKSSs6X0HD9s4hib5u7khYoeZbnWVFrlqbc2tFq/SycspiVv6+bCqzKZaHYonYP4fBn6avrPFevX3TPSig58HQW6LpCCMGIKp+ZlMGW9owkXKpKaTFu6LskzBua3fQeC6OsA3vdui66Pun7JNhynfKWveZp71kJJc9kMsjn8z5vREPaGo0E4hZRw/jKRvB6tXbMELTf5fP5GIyx7EPIuhDPW2H2JPE3y9V8dY3mKpam4iqLYFft6HtqXbTuapEtRFNHd1lnVEWfpZI2mPW3TYtWZ9zea/vaPtcuJnkleHJ2sOZvs/FV+Si0yMpYpI1sMhXKZvB6zcHWCGpafkjSShutB5VCgy2aj03aUwcC03orlYofgFZZyLwwt2Y4HAaToJhLsiiIpp/pd0p5LrovqYwkZzM0e9i2WyTLOtgroeRWufkZgFhmIbMA+V0ul0OhULjCddvOZjl21QwtclIOeFKnhpKEtDxrHXO5nB9IVFC1avl8HhsbG2g0GtjY2PAKHlrlQ5lMJhgOh+j3++j3+xgOhxgMBj6Tcjgc+msA+OxFDqiQstn3DSVIpTl9af4A77HQUI1ZqC6hMtVn08+SZCWUXEWnef3RaVqxdGhgLINJ9XnXcdbSrIfCAvv5dDr1GY69Xg/j8Rjlchm7u7vY29tDrVbzg3UymeD09BRnZ2dXYBE7lwO8VCphc3MThUIBzjkMh0OcnJzg+PgY7XY7Bl1snaxyWdiS9n/SvUnOYOj6tDLt3/bapHJDslJKbhtK8anidH6v0VC1jGllL/s5JdSJuiTNShIfTGuby+WQz+dx69Yt3LlzBzdu3EAul8Pjx4/xzjvv4Pz8HMPhMLY6KO05Grnd2trC5uYmarUaCoUCOp0OWq0W+v2+93fsLJMk1mJbZUyDFWqktA1D5SQ92/6dpuCLZKWUPKkxGArWhcLa0DYsnlZW2jOXqRuxdNIyr5BvkM1mkc/nMRgM4JzD7u4u3nrrLdRqNRweHuLhw4c4PDzE0dERoihCsVhEoVC4Ev7mbz5fZ7bxeIz33nsP3/3ud3Hr1i3cuHEDe3t7qFarODk5wenpaczfUOdv0UyX1k5W2S0uT7LgaRJytkPW3OpBkqyUklNYaXaiVXAKMbq14iGnURWCnZ3kkOnvkCXnulL9nKKNzjwcKur29jZu3LiB3d1d9Pt9fPOb38TR0RH6/T6ccyiXyzEnmCnAoala66iLefv9Pt5//32cnJxgfX0dW1tb2NnZQSaTwfHxMfr9vp8VgeVW0oecRH1f/dFrktrPXpPUjgCuEAs2yW0ZI7UySq6Uma5oUUW0FN94PPZbN+jUTdGGp/IovQeEsWcajrVla+fRqeT78PNcLodarYYbN26gXC7j0aNHeP/999Fut2P3qHMYos34OS08oRPvJxSi43lycoJut4udnR3s7++jUCjgwYMHGAwGyOfzsVkh9Bybb8L30hlUf9TIhPpCRdtZ39W2qaZBh2aeZSDLSii5tdxacbXUei1/60ogtWqhcgCgVCr5xCsOEIt901Jcbbn6bOXvM5kMyuUyZrP53im7u7soFos4ODjAw4cP0ev1YkyDc/N1rawn6wdc+h4K1UJwSRPaOKC73a5nXG7duoVSqYR3330XnU7HX7cMHWqfo+3N99B2t36VlskyrN9hfwNX2ZgkTP/KOJ7AVSzHsLtdCGxl2dyNJAYkdN0y+N5iRioiB1ClUsHe3h7W19dxeHiIdruNSqWCKIrQ6XT8zKVBGv4ulUpB67Wo/ha/jsdjPHjwAL1eD/fu3cObb76Jd999F6PRKDa7UZ7Wn2E9OQjUwrM+miinfkFSmaF3W9Z5pqyMki9yfpIahWKhjRWbf85npg2ONAYhZFE0csnoaqPRQD6fx/3793FycoLhcOivqdVqaLVaaLVaV6AIg0L6fyhfJdR+IYs4Ho9xcnKCSqWCra0tbG9v4/Hjx7EosL5P0kDSQZgm2meh2fK6imrLvo6sjJKnSZJi24hc2v20IIQEdL4YbFqm4ZIgDD+PovlWbrlcDqVSyTt+rVYL3//+9zGdTjEYDHB6eopWq4Xz83N0u90r4WnNRS+VSqjX69jY2EC9XkexWEzdyYCifkcmk0GlUsFsNsPDh/Pd+ra3tzGdTnFwcJAYcElaZpbUH0ltpoZkEVuzrKQZICsro+RJoWN2Nh1TaxFUgZMcxlC5SZ/Z70OOKX+HHFY6xOVyGY1GAwDQbrc9xff9738frVYLg8EA2WwW6+vrqFarV3JKVJG63S76/T6ePHmCUqmEGzduoF6vJ9aForh4OBz6FN1ms4nt7W3cvn0bk8kER0dHqe0QkkX+yiImRJ3a6z7jOvAGWBElpwUEENuKDbhc72mdSOI7WmImajEUbqds3sNIIdMErLNmE7x4r67SYR2pNCyjWCyiVCp5ZxMAzs7OcHBwgHfeeQfNZhPr6+vY29tDuVz2lCF3xKLzaXf0ZfCJeS/j8RhnZ2dXtq4LOXA68Pju5+fnePfdd/HGG2/g9u3bGA6H6Ha7/tlkorizmMKO2Sy86RCfqYus2YasI/dwZNlsQxovZXA0FmGdWIV1y/hYK6HkVBArrDgbhFMoE490yiqVSl7ZFXPbsiwzoRbFXm934lIWQrMJ2bFkRCqVCra3t+Gcw/HxMU5OTpDL5XDr1i2v/HZ5GxXH7h6gkMNSeqpYaTBC93rku52fn+Phw4e4ffs2dnZ2vIJrm9u1qKH20+9UufUnaZGKKqg6qDb7MgmaLAt9VkbJKZbaY56GTn1Kr9lVNqFFEKoAoe0XbCNRsa2Dqp2mWYMalcxms6jVaigWizg7O0Oz2cR0OvW8tAZ4OLj1AALd3Zeig42JbBaSKZ63ouWxXWezGU5PT1EqlVCpVDzduba25pO7dIZYRNdZR9Iqufo9Ibxv3znk3Np6LIvxV0LJOQVSsQaDAQD4Fe7ch1ADRtYSKm6nsAFUIZNEG5B/W4vF2YRWic9jZ2azWTQaDWxvb2M2m3nKUGcAbkVXKpX8DKXPS7J4oe/5OTMfk7Ijk95zOBzi4cOHuHnzpn83zjTj8fiKjxBilKyoIltLfh1ZRJGyTsvISii5HfW6an82m6Hf7wO43LdaQ9JUIMWvWu6ysmwHhqwLJZfLeZzdbDbR6/V83arVql+3yZ9sNusXfoQih3wmBz8VhTMbhXGEpG2YSUGGhH6FMk3MaAzRlNYQhERnx2WuT7o/acaw1y6SlVHyUqnkrRUdHsIL7WB1+GjBLEZdlJNiv0tqKF2MbK2a3QoagLfQ5+fnuH//PgaDgWdOisVizJHTrel0RZD+UDEsfLL70ADxnJyQKKTiO+izG40Gstksnjx5gm63G3vnZViOtM+TlHaR0l/3uUnyTErunPsegDaAKYBJFEU/4pzbBPAvANwD8D0APxlF0VlaOZxqAXirotZL/6a1Z/ITp1dSZGQcWK46kBQqBs8RIvTQnJmL94uxLMpfr62tYTAYYDQa+XoUi0Uf3JlMJt6ZVkeZA4TskL6/RgMtFOOMxZVA/C607THLJ8ZnGgPLUiUZj8c4OjryzyAboj6ENRqEWZxp9egXGiabE8Oyre/E97ADQQe6Fc33YRukQdHnYcn/4yiKjuX/zwH4/SiKftnND6n9HIC/vUxB1jKRaqLFAS6ZhEKhgHK57Ck1do4yMdrISvWFnBy7AY6lFu093LOFCl6v1xFFEc7OznzeeCaT8XQgB6nmwFP5WJaNvvId1OJy2R/fmfUHLsPoLIv3WV9FFWo6nWI4HOLo6Ai1Ws0HsZjRqIrJganLExn80vdhXSxDogOZ9bYxjlB76+csRzd+WhQcexFw5bMA/uLF378O4A+wQMlZeVoeTVJaW1vzOHc6ncbSUguFgs9EzGTmJ0OoJQEuG9Ius1JoE4I31kLyN5VyOByiVCqhWq3638PhEMViEcPhEKenp57atPQoOf0oujw6xnYs/Q8OfK7j1HWqocUbfJd8Pu/fmVw029S+Z6VS8Za8VCr5JYEAPD7XdgAuDy0YjUZ+Fuv3+17pdTCz3knnHIWgGt/FQsUkhU6DPs+q5BGAf+2ciwD8r9H8RLe9KIoeX3z/BMBe6EYnRxyur68DuHwpjlKrXPyfCsDryOlyGmPonk4sgyi+0lGcn10k6hTyhwrIbEPn5iv/mfTE+zS1VwcWyyFzFGgfX1cOWg2Q0Nmk4oSmbJ0ZQ6LMzHg89gdrDQaD1KMHGVwiI8b30EFrE+tCYn0lq+T6HWWZ/Hcrz6rkfz6KoofOuV0Av+uc+//0yyiKoosBcEUiOeLw1q1bES2UZuQxW4/porlcDo1Gw/PKevQJA0GarkooQYzpnPPKwuvsfoPWOQMueWoqJlkSHYTsZEIPHVSMVuoCCt6nZavwOg5WDXJpsplCgkwm4y04683BbLG49IMfjAxI9fv91Hs4k0UXlGMmk8FwOIw9n2UmpWtcR9T5T1rm+MLgShRFDy9+Hzrnfgvz48YPnHM3oyh67Jy7CeBwmZcYjUYxBadSaMgbiG/UyYZmZ/Bv4mEqNzuCGI7KyGeHuPWQdSHfTv+Av3u9ng+ikO5UuGA5dYrCImVT9Pl6n2JuXsdncImd7p6rAzg0zetg5LspHRnazoLCNq5Wq4iieNqwMj9qvELsShpDE2LFkpzMtBn5qZXcOVcBkImiqH3x918G8N8D+AKAnwHwyxe/f3uZ8jj184f8MR2hXC6Hfr+Pk5MT9Pt93+FUJnYGO4dl6kJnWgHe0+l0rjg/aoFUCbURWVaj0fCO72w2w2Aw8BmOhUIhFsRifajwIadRFZxwiKL14m8Ofh0Qqhjqi6iyarnKyOgg4jPZJjrYOZsRMjJeoTw/y+TgsU61Mle2zS0Tw+spOuOzf9O2+nsWS74H4LcuHr4G4J9FUfT/OOf+CMBvOud+DsD3AfzkooKcc9jY2EAul/PKTYw4m838MjEqzXA49NQdHTs6ptphxJYaEczn86hUKv5vNnqz2Qxuy0aLxagsI7KEQZVKBd1uF71ez1NpNvLIvxkEojLQKmlAiNaxXq9fYXu4twpz0jU5S3EyLbFuYmT3g6Gy8p05WFku21sxOnE235tJV4RopHMnk4mfVXTgK/VHZbaYng4w99NhfdVxZr/zWjJBSfLUSh5F0XsAfijw+QmAv3Sdstixs9kMnU4H3W4XhUIBk8kE/X7fW+e1tTW/kp24mmsZGWyh5WQjUTmUYhwMBlhbW8PNmzdRKpXQbrcBAEdHR5hOp8jlcrETodXBtDkiHBx62rNicGb+ra+vexzPTtXT5Vh2vV7320rkcjl0u12fIciBaa0t24ILLTjT6M5aIahy0V9+ELGN7I5iSgRQqch0qYNP/4jYuVQqoVAo+FRh6zDq4GadtN4hWMK9Ztg/3W435q+EZCUintPpFA8ePEA2m8XOzg5u3ryJyWTiHU69jlZDLREbmoe66jSmQYdWq+UtAXep6nQ6mM1muHXrFvb29nB4eIjz83PfyDoN0jqyI62VpKPLGaVQKHjGolwu+9xxQpnvfve7ODg48AOC0Gx9fd0PeA5aWk+eXK2O7mg0wmAwQK/X83XRpC6L8y0lqLw5ZyG2Kz/nce4cyCHalc9VB52wK4SZFd4oswbEszF1kOqAppHjzJYkK6Hk2WzWZ8ExckkLp+fLs+G1UdQy0oKzHOAy6457fdPBdc6h0+mg3+97xa1UKrhx4way2SzOz8+v5MEojtQMPdaF6QacmXZ2drCxseGdUuZt5/N5bG5uYn9/H1EU4fT0FOPxGNVqFcB8duj3++j1ejg/P8fx8TF6vR4ajQZu3LiBzc3N2LHsnBG4ZZzF3ElCyKAWk0rHqKoyVFRYxir0/fmjtCl5cw3wqTDdgX/rDMmZzl5PVq3T6XgYlBbtBFZEyTnKc7mct6y0iDanWxuXzIlyulR4lslGYEorr2EwYzweYzAYoNlsolqtYmtrK+ZQUazjZdkXIJ4oVSqV/PNnsxmePHmCJ0+e+MULm5ubfvU8p+harYZOp4NOp4Pj42Ocnp5iMpmgXC7j5s2byOfzHv+rc01F7/V6MeOwKBpIy0lYZ4NLelguMbk6z8ryUGF1v0oO+qTnc4ZiHVXJNZVBhXpBDM4Z+UU5ns9NZrMZWq2Wt8J8OTpZiqf1xVUZlW9Wi8/GojPEyFyr1fKdOhqN/NrJ4+Nj/5kVWg4AsYO4OBgYmaXiPX782OeFlMtl/Jk/82ewv7+PdruNP/3TP8X777/vA0iTyQS9Xg+DwQDn5+c4Pz/3jt/Nmzfxgz/4gyiVSjg6OsLp6Sl6vR7a7TbOz89xenqKdruNcrnsj0pXSGGjqRQbTdSBrPkrHIR2oQj7Tn/roFK2xlKn+nzWQa/RiCnrRvjJGYIDyaYtWFkJJVdGhArZ7/extrbmw+W0MFEU+SVmTGWlMwpcWlPFybPZzC9eoMKVy2Ufgq5Wq6hUKnDO4ezszAc5SDUyp4MO4HA49JCC+eEa/FlbW8Pp6SkePHjgLRkd6M3NTf883VsdgN8mjpb8/PwczWYTX/7yl/E7v/M7HjaUy2VMp1M0m00/GMrlMnZ2drxTRr+BChlyzKhIZE/4WbFYxGg08rCEbIc6pmTAGPafTqe+fThIaNk5M1M5NYeFRiyJSrVUK8vf3Nz0/w8Gg0Q+H1gRJQcuaTLd54/WQnMwlE9VWozTtLIhxIVra2vemWNn0npGUeQ7sNls4uTkBPV6HY1GA4VCAZubm9jc3PRQgVutZbNZVKtVr0S0luqMsuNJX3LVfj6fx/b2NvL5vF/RT9aENB4H4qc+9SkMh0N885vfxIMHD1Cv1z1eZhR4Z2cHe3t73orrSip1Qm17az35GXBpgbWt+D2dSbY1P6NPwoGrzBRwCT+sxWY5NiCnim+tuWZ1AuEtplVWQsnVMyckUE8eiOd76KoVG+gB4gGh4XDomQt1bO2i5H6/j8lkgq2tLe8s0uL2ej20Wi0Ui0VUKhUfROKso0EadZDL5bJ33n7gB34A+/v76HQ6ODs7w9HREYbDIcrlsp81dMu77e1t1Ot1VKtV5PN5vPnmm94ZOzs783CL9eR16owDcQVS5VL/gt8pFCP1SQaHbUyopwqvsQRbDpWSbJMyKtq37E9V9hAEUf9H4xErD1coam1shItKZfGX7Thez4YlTiafSqvKLEZy7t1uF5ubm9jd3UUul/Mb/hwfH+P4+BiDwQBbW1t+/Saxt66uJ4/N78h6tNttfOlLX0KlUsF0OsXZ2RnG4zH29vZ8Hg6FCkXIQr+k3+/7GEKv10O9Xsft27dx69YtbG5u+kUnoaSoNHqN7WZxsg2l87fCglDQy1KRqsAKQxTL6zN5jeYgqVAP2L92YIRkpZScL2kjc5rLQkpxUeYeAJ9GSo6cM4CGhDmD0CnlNF2pVNBsNtFsNn2OzPn5ud9TpVgsotvtxqKvwKVzSk57a2sL/X4fBwcHePTokV8t9JGPfAS7u7tXFEqnc343Go3Q6XR8Ku/+/r7fHZezDmcRHegWMiyj7LSwHFxaDyqWDRZpOF5zy61Tq89ZRhcIubTui94hJCun5PpbhRZBE5pCOJPwRSN0pPU0p5z8LR1eOlr9fh/FYtEvHNBwtT5HI42cljUIwlXwVP7t7W3s7Ox4SrBarXpGRfeK4WzFaCFnCg7CTCbjMbLieD5boUDSzJjU5nawsc30RzM67TOtQtoUAh1ESZCKv5OSw/TeZRV+JZScitRut723zUYljuP0ReXUl2RkLpTL3Gw2Y7OBKjVxtAYg+N1gMPBWnlaFrA9wuQFOuVyOBa84CGnVlHFoNBp+YHQ6HT8YCHsA+NmESq0MBZWG/oU62cpCaN456x6a0qnI6pwq/67XaWyBA0KVmM/m9SyTLAvXv9KPofOsdVUfTP0qXXBBhsdCldDBvpSVUHLiVyock3v4W9cLEnJoAo8qIqEMO63b7XqFU+7YJgyRLmRH0rmk5eJ9g8EglqPBeiiXrFBBKS52jg5EKik7nEv6CBlYd31X1l8tvw2aUZaxdqq0NB4aVQQQ2wteAzhqtdnuClMKhQKq1SpqtRpms1ksSKaJdZyNCEnZfkyI09mZUWXO2ovecyWUfDqdotfr+XWb5HlJVdGKU8E5EPhimm3X6/ViDgs5YjqcFsurldLPyElTuQltuMSOSkErzWASO5l1AS6tj2JZ1oXrKJmTwrwaDjbWQ7dTU99Fl9LpwLG+TYi6Ay4dP80ZyWaz2N3dxZ07d/zsyXJ1wYnCDQ5yDgTWh8lWDHLRaLB/ueCF705rTyjG2SWXy8VSBNSp5eycJCuh5Mz10ACB7slH66x0XbfbTS3PUmkq2vHKLISus9/T8qoFC+FRHSicolUpLJNAyo4RPd5DCnI2u8zK06V/fEed5jVXJO39bJuxPOWiGQhSbpozluJxAP46PV+UMMOG6DkwOKh0RuSGTJyhlGkhI2Y59JXPQqSjxZfW3GoNalicqYqin9vgTIhmXEbByQ1bUSpTO1oZAQ4GYlHuZFssFlEul5HNZv1+5bRopVLJnxLHsnq9HmazmZ/abbtp2DztXRYpum1TngnK5yiEZLg/5HiSqeKJHpyxdLmhQg8ObmJqPpfvpauddAbV/tFZJSQroeS03hRNptfpVDPmQjQUGyGJl1VnNTQAbJ2SIoUcgJYpUEZAHTl+xpx5Rky3t7dx8+ZNvxchcBmmHgwGaLfbHipQcXTAWwVTq6qKvwxlZ2c93qupvOwba3jsDKUDgjCDA1dZGrYlsy0VMrKNFd6o70MfQXcWTpKVUHI6lJpvQSul051+RiWyHWm3bqYV0hGvnrmlokJl2romzSC8nzCDgSGN+LXbbTx+/Bij0Qgf//jH8eabb2J9fR2TyQSdTsdbdd0SghjVzmisjzrmoRmP9Urjy6lgVGA6dzaiy/fgrKLBHjUuDGCps6z5+LyXbBYP8FUGjNSsRlX5zuxPjV4nyUoouVpqbTTr8SsLQ4uu4XXb2ewQ5kEztZYDSYNKljrTHGVLlSllxs6lQpDXZgfSgpXL5RgP/+jRIxwfH2MymeCTn/wkWq0WDg8PfY77YDBAo9FAvV73A0X3T+H76ZI3jcLSolJhQlFQfR8mTpGbZ3IWHXYqmi6DU4NEo2H9AdKhJAo0lqEsGA0CHXdacDr2tNoKR/keoZRclZVQclUS7mHCaCIQ339bOWE2pHKoNghChdFn0VIBl5vbaKOx7CSxg08tKxkC5Xk5+zBrb319HY1GA9/5znfwB3/wB/jKV76Cmzdvot/v+zTaSqWC9fV1bG5u+tyaTGa+YkdXTKnlph+gVp/vGbLequh0KtWi63204joD8ntbtp0xdMZUWKVWXvuWEIZ72NjceHWoNfszSVZCyemAKC0HxLdr44tR4a1lslaWop2h92oimHa2Rtm0rEV/h2AE6zsej9FsNv12clT0t956y5+teXx8jGq1io2NDWxvb6PRaGBrawvVatVz1ufn5+j1en72oij3b2dA+15Jg1jf2/otti1tu1voZsu3O3gprNL6KWtFBdYgns6+WrdQZFRlZZQcmFeW1BM7ktaJUyEVx4rFySHrzGcoLtf70yTJWlmrxelcWQbCFuZ9l0ol3L59Gx//+MdxcnKCb3zjGzg6OvIpvjypgmwLk8TosOoqfRXFrKyT+hyh913klIaYmaR7Qn3ANtFF1WrJLVlAZSbUCvVLiAxIk5VQcgB+OqYQY3I60oANI5D25ULO4rIdZCVJiZPKIZPC4IZaci7ParfbePjwIVqtFjY2Nvzih3q97iOCxNhnZ2d47733cHBw4PFxLpdDvV6PRWBtHdTaWgUN/Z8mFhosy9QkUba2XMX0oR8OUl2FtWx/qKyMkjNyx7A2Q7m6yY5y08TWIQlZH8WWy1qnkCInlc/66ZYRxOWau3H//n189atfxaNHj5DNZj0+z2azPr+ce64wtWBvbw9bW1v+GQqxrAOp+Fzfm38vO8htuyX9HxJLY1KZdeU+lZx4W51SADHmRJkxpR/5nFdm0YTml5B+owXUZW60lMph09qw0xmp00GhG9+oo0uHSiOGQLzhaGEVTxJKqT/BjuASO24GRBzOpWk8iKpWq/ndwTRKSEptOp16Dp3sg6VQNYWAgz+kRGoFLYYOwRjCLl3LqSF0bXMLQ1iG5pur5Sbc0vrRCaVia3/ae5SZYZ1D8I2yEkoOxFeGK0+uQRZ1sFQ0MsrOt04SZwpNIOI1Fvqo5x/CkOStKer1swM4WLkgo1wu+wHBg2K5oILHizM6yA2LODgsg6RKyzqS3qMyqZXX+ock9DnrY2eMUFlqsUNWWdvW5pknBbNCz+E1VqlDDI/KSii5vhj5Xe1Yu1Mr7wk1rCq4KrqO/ND0FsKuIdyv1/MaIJ4zYtMBOp0OKpUKNjc3/XK68/NznJyc4MGDBzg4OEC32/WLPGq1mt9n0TkXW6itA1OpPOJ08srqWF7HUVNoGFLEpLbj4A9ZVKuoLCMpSsk+tGwVEB8ki/hxykoouQoVx2JLVWSdrilqZUOdqFZJhdO/xdtKOapEURSjsNTiJWHewWCAw8NDZDIZbGxs+JX6xN+9Xs9z36VSyS+qsOFzG5VUBX5ap+w6orNZSJKU1sJL+50VGq4kBmkRZWhl5ZQcSHcCiYcZHbP4LIl2SgocEJOrgrAzNVlfy+FvtfahwaDS7/fx+PFj9Pt97O3t4ebNmz4pa3d3F2+//bbP+LM+gl19TxgUWmkfkqdxONPKCgnrlfTcEKQg+2ShD3D1jCFKmvVPkpVR8pATCcT5XP5ovjEjibSuSZJkARjKTqpTqGMUjqiihwIVKtPpfIuK09NTrK2tYXd3F+VyGd1uN5ZqQKuuFJs+l0lTmn1nAynP23qz3DSx0Wbeo2s/bdlJSptkrPgc3r+MrISSW0eFS89o0XRJHKEMG4EvrDtlKW6nKIyxVJuFHprpqMwBf5huwC3KdDGzWqZQcGY8Hvtdut544w3s7+97NqXX6/m9GVmWTSXVgaRTOi0p20ZzcfT5lJAyah+ocvNZym+rqAFSx15/bHurA6ltpu8ZelfrJy0zoBcquXPu1wD8ZwAOoyj6gYvPgscYuvnT/zGAHwfQA/CzURR9ZdEzKGw8dirhhWYPApdb/lJpgMsEK20YFVJgQLxTlJmhAtPpbbfbsRQD/qb1J8Oim1PqQVK6RpF1IDxqtVo4ODhALpdDrVbzZXJV0Gg0QqvVQr/f9z4IIQqAmB/AstVZs1g9ie9OUxALx9hONBh29iXFaWcVvrdlUtSPsWXZhTKaaq0DW5mkJFnGkv/vAP5nAL8hnyUdY/hjAD528fMZAP/k4vdCUUxH5aFSWjihvLWuEtENg0Llh/ChhTFs4Fwuh729Pd9JOkNoUIMrmLiREJUYuNx5is/RHaScczg5OYFzDru7uz77j06o7kuuARG2j000I76l9dc1syELSAkpK+vLdlTokMawaCRWB48aGH3/KLpcvmehVhRdLqhQ1szmjqfBGspCJY+i6N845+6Zjz+L8DGGnwXwG9H8Df/QObfuLs4PWvScJEnC0fycaysBxJZbmXcI/g0gtg8glYQWQ3ekUrqKnaPO7iJRi8qOmU6nODo6wvn5uefD+Q60npouS4uo9KFCKna2JqDxnZIcPyDdmbRwQA2MyiLKle2kygxcUqH6PJbFumvuEpVcnX3rvFp5WkyedIzhLQD35boHF589tZKrsMG5ega4PF6DUIN43t4Tcor4PcvRfHDgEufbhbdseHUyNRXVij6fHaYWj5mF7EjN2dF68rl2nxXLKVM4eyQt9E3DsyG2RNvJ3mcxNZ+vdVe8rpFUrQ9F15hyNmV9FQ5S2ZMcWOA5OJ5RlHyMYZo4Ocez0WikXmtfwDo6Kmn5xSHrwzWXxPe6l6FaQOuUWedKrVBIAYhJbfAKgI9+8mQJUolUUEIHjdRqNDY0gG3Y3FrLRZI2cEJMCd8jdA8HhmJ79bFCDjFhms56iu15zTK8+dMqedIxhg8B3JHrbl98dkUiOcdzf38/UgZArat2Cv+mRdUpMooiv8MUr2V5LMuyLpx6T09PPdVFBQMQW19J68rcCdaN13DKVFxLYdn2O74LoVEURX4XAk7LSi0qHEhzJqncLCcEU5JmHB3AWq6yNMqIaB9QGXUG0IHI3BQqZihSbSGS1l9nLfbDMor+tEqedIzhFwD8gnPu85g7nM1l8bhO98qy2KnNri2kks1ms9geJQo7gEtrqlOfYjs2quak6K60ADyjoxE5LvKt1Woxi6lWSp04dWB1wHDTHaba6vpGvrtSkmql7UxC0aWC3C6CQiVlWTojKHvBa9WKcrmd+hhKW+pSxJAyM8LMZ2rKBv2f0AymA4tlcUezZ2JXnHP/HHMnc9s59wDAf4e5coeOMfwi5vThO5hTiH9tUfkUMhZqMSialTadzk8V4/o/VWbnXCw5SjlmrhnkdfwhrtM1jsS9tEiKQVk2t81gPbrdLkqlUhBLA5ebx6twJmFdS6USGo0Gjo6O/Oyg6xrZTjafXq0fB7I6r7qihu/L/H22GcthXdVCU1HtTmGsO+s/Ho9jx9ZwgGndGcvQDUWt6DGN3DiVBkEddx2Qz4TJoyj66YSvrhxjeMGq/PyiMq1YvAvEo1oWe47HY7RaLb+4lcqqU7q1AqpgbBhm/XEVz2w2P9ZFIQvrwgaldeWR6Iq3E9ov9h72O3UOybAUi8XYulS14vw/hMM5qK2CUmG0fVThQ+XQkuqiBp39aDDsvXSKORC5VpPlApe4noaN9VaoQ+WezS4PPdNlcEp1pik4sCIRTyDOj6pi8AUUjylFxtX4tBK66xbvAxCzhtpxa2trsY1vyCpQmcncECqxTvy71+v5/BKuzA/hWb6PFd2XZTgcIpvNYmNjwx8KQKWxvoV+xrKtH6JBLr6/tkdo4C3qI7JNukxNZ0NGbfU0aiq85sTze2WtdK+WTCaDfr/v17UC8GsM+L5Kp6bJyig5xVoW9cR16iQ0UMw6Go3Qbrd9Z3BnJiokhRaZFmU2m68Od875DXy4b4huPkkLYp1bm+Sf9E52tuLfxJatVgvVatUfJ85zOfkMlmXL1r8txFDfgAMauDw6MsQKpTmlGkegZScU4wzAAR9FEdbX17GxsYFsNot2u42zs7Mr+54r+8J+5Ds7d3mGqCZtsS5pFCplZZRcp18LX6ySAIhNu7QgzB+xgRt1jHTKZZk8u4flzGYz1Gq12ABR5abzFUWRX9VDq5V0QoJlVfgZsSy3ztBO1HuUeSAjY8u0im2VXNtVrbx+ZkUHgioS8bjuM1Ov13291tfXPeTQk6gHg0Fsa2y2K30NWuput4vpdOpPBNE4gjUWSlaEZCWUPIqi2L57upxMnUOrHOygYrGIarUaW0JnOW9+t7Y2PyueG/0wd4QLGija4MAlTODUS2aFdaLSc0BZ3KwBJK5fpWPZbDbRbrdjSkx/Q6d4bo7E9iELQ+vJNtKAjTqMdMBJVZLJYLsrU8KAGz+jFeWA5j3ApS+gR6jzNL1Wq+W3gC6Xy9jY2MDGxgba7Tb6/b5vYzqbnJm5NyYQT4/g/6oPacwKsCJKDlxdxUNLq161Tsdra2s+MYqdrPiayqJwhucAabYilbXVanksmMlk/NI0xfbEkFQ6Ko4qlfUtlLHgO3KpGk9aVgeaewLq7q1JNCStrFo3IJ7kxGs4+HUgqdFQ7lqpQeAyyUpJACq+7rBLn6RYLKLT6fhZkfdzSw2NhDJ3hfsn9vt9dLtd5HI5f9Qisbc6ysqrL/IrVkbJ7ZElwCWXq9M/FZq/+YJMd2WDqEMEXFr/0WiEUqnkjythB3Pq5bSp9B5wCRc4AK31UKfUCgcBhb5Dp9PxVkp3sNITly3fz/uVReH7WUYKiJ/Cwf+5gwDLV8eU9bUBIYrOrMTPlr7kDr66O6/y5vl83s+8tP7cm57Gg++lv5U6ZH9wEKfJSig5HT5KiPelRVBqi9uxTadT36hKM9EqsSFqtVose5EOklp1lqU4VTF8CFur2AbngOCzeKz4aDTy1guAZxWoCKTPQuVrsEQtvQai2AY0CMri6GDQBdKh9wi9Dwc7d5YlVKPDzhmPxzbaZLa1tTW0Wi2Uy2Wsr6+jUCj4TfqjKPJpFrqzAuttDcmiaCewIkoOhOk17UQqvlpnnQppkW3mGhuYUyL3dNHjzGm1aXGIFXV2WZZus04R6z0ajXB4eIjT01NkMvPT4Vh/5pFzsyFlGkLl08prCq22gzIguh+jwhgqt7Irof6wrAufb/dLUSdSeXEGi/jOPG+UNCSZE2J33b2WbUPjoydZaHzhldjwUx1PbXSyG8TAismn06lf1ECHSjME1TmlpeYx41EU+SMDmRym2DrpdATLVKSJfs8DZnmCMv0MiobYyRYpBrVtpTk42i46y2nsALhkQ/S5Fnsn1d+yWmzncrl8xdLyWefn5zg/P0cmk/G06Pr6OtbX1z39y4Up3HSfBw3QKabzyvYilAnNLmmyEkoOxHlbtQhkGMhqqOICiHUucHm+EBWAg4Cf05rTWep0Ot4a0HkkV06LYTluG1DRHA1ew+u5EIKnvdVqNf+O6hgD8BRosVj0uJQzkjI1SdvEqQLy2Ww/je5GURTzR/gewGUMQZVKo4zKSXP2UGjE/WUYp2DgjHVW+ELYw30SadCIzUkAMBjEa2y8goMkUbeurY0vQHQK1s+odFRCYkh2ACNuwKXycTN363hphJOKxOuBS8aD4XV1yCxHr5bEsig22YiQid9rZxK3khViOJzMkSqThWy6hpO/9TrOSGwzDmLd6F5TiXmtDXSxXLaDLj3Udmcb8pj3tbU1bG5uxnA017HqbMN+1E2UOAMxIMf2I+NiYwRs1yRZCSWPostsMgpfnta72+3GKDAA3gLQUaPyhpKkNORP4ZSrnc9yWS/lZJMCLlpnlqtBEg4oVUQLOWiFdeVLCK5wFrMsj0YgrXPGwAvrruF91oGiyW4MtdutL+hYss1Zn/F4jJOTE+TzeVQqFQ/NuPU0l/QB8Eo9Go1QKBR8lJewkmF9Uquc2abTqacidTYMsXOUlVByIBnnWs5Zk4d0mlWLHXJEQg6hfY4VXRanzlDS9VQADgzCodlshnK5HFvJooyI4mQLe5appw46mwKhA4rvou+Q5HRaSlIpULY1HUy19qF9DhlBJrvFQcNUCgB+70edZdWn4D6SCt3oQGsyW0hWQsmdu3q0tyqjhQS8hw3K0W+XvtnyQ6NdsW7o+bactP9pmZjhxzQBde44MNWaKiOkbFKSk6Vl2QFDJoMDijDPOpz6d+i9+GxaYio068/3JBRSXK7P4yxVqVT8Xu1MY6hUKp4fd875RdykIelL0E9itFp9C6Unk2RllNxaX9vJ1jqpxaYzE8oC5L02uBGixqwk8a9J3ryG0tn4hCCcdoHLgUXLp6uc1PpaSKEQSduBfyvUoOJxMTYtIGm9RUEUzRNhXdTyc8Yaj8d+w1I6mJpnTija7/d9agLLIQShstMyU6k1d6jf7+Ps7OyKn0KoksawrISSWwzJv9Wa8LcqJJ044FLBisVibHGERvH0GVQQfmdnCd7LzzUiyDqzs5TJ0XrqSiViWB1sivVD7aDP15lM66KWn4NGr1fO3f7wfn1vHfhUdI2aahtwgCm80hQM+kycaWmhAXgmhs5qs9mMpThns1l0Oh1fl1arhXa77d8xn8/7+1+JYFAUzXlypa/YSWQhlD7j9ZpDQcs+m818Bls2m/WnpynbQRYjl8vFqCzlnDW3I5PJxA5pVcePzpBG/qbTKarVKgqFAprNZoyD5wBU7BxiBmgVgfiGmZbJUWWkRVNKjXhVw+e0xPp8/q0JWPybwSpaW40+q7/CgA2hCZW0UCig2+3COYdKpeJpQc4ErVYL4/HYHzXT7XbR6/ViWFsTyfgOHJTK9oRkJZRchSNZI1oapNHNaPj99vY27ty5g0wmg5OTE7z//vsYDoc+XEzl17xvRjaVxlMFUmXnc3TZm065/J9hbMIBjdAxAMLOYafpjKE/nNq186yjbXE2o7jK3zOjkYPe4nRi5tls5pVvOBx6hoQHANBRTDruPZvNxgYCFZoWW1daaXDu/Pzc57rQ8WTduIiC9WTaLmdNJtUxRSNJVkbJQ1OOWjBeww7kZ6TqSEFVKhXcunXLb9LT7XZ9kIWJV1QAC1FsHdRaAeFVOWxwDgAq09nZGVqtFgqFAjY2NmKDSGGKlgMgpsTqmKpfEYJeCk2otBxIDEJpUIwOH2dFruihFWdE+ejoyONoKvLW1pYf9JrOzDgDlZUBID1evVaroVKpoF6vw7l5ztLW1paflbl+l3voMFikxmM0GsUoSVLBSbISSq6WjR3IxmPjauahOk/soHa7jVxufnDU7du3vaIdHBxgMBh4eKKQRWGEBoqo1LQohDLWGaSoVWTdj4+PMRwOsb+/H7NiSc6e4mGLzak0SffzMx0oGoXl7EjczmvZds1m07MazjnPW5POI/a1mZ3ExWwvjWayPZkyXK1WvQNJWpWDRrMLqdgM+gwGA39Nv9/3OL9araLVavng38oHg+ikUXHV+6flUmupU7ouyOWUqznm29vb3pqrIvG5quRUCOJMwiNNbdVsPi2H9w+HQ58PXa1WYxYmicUJtQetMbD4nEoKlZD30l8hl6wLN4h7GXjZ2NjwgTS+OxdpEDsTHhHfEypoXrn2BY2QOsQKPRnxJIybTCY+Q1OTzwgZ1UEvFApoNBooFot+wUWSrISSA1fzoQHEMtCoaHwZdTY1YjYYDPDo0SMfgKnX6945UTxtAx58rv2OzyQMsXUGLjfzoUXhkYWaGkBLqh0VKkudXWVhbDBKB4v6KGRwOAMqJUlnjr4JYUytVvP5Oq1Wy+/POJvNsLW1hVu3bvnEKqYHs02Z0amLwfnefCcOEjvj0SBxGwvtU62/whKurKLyA5dBuyRZCSVnZFAxJK2uWg1dEa/bqLEDNRCTzWY9riPnSsszm83Qbrdxenrqz8jM5/Oo1Wr+2br3B3lgVSx2puahR1GEzc1N5PN5b8E0oEEemw6VdjoQXrPImQ2I7w8IIAbp2F40Asy/Yf46FUsdRLbTYDDA/fv3fRrwjRs38JnPfAY3b970ziMNDTlve1odZ1/OhLp6qt/ve+6bx8QQLnGAcNE4octkMsHh4WEMRpElc875AFKpVEKlUnk1LDlFHTKOZuByyrbMC3A1M5BKwumPOSRUDio0AI/pgMvwPZkRIL55P+vAIAstC/l5cuL0A3iqMq0mO4YzhQ38qL/AtlCcrlFE4HIfE9ZPBxF34lLGRROcBoMBTk9PfaprqVTCJz/5SXz0ox/Fzs4Ostn5ssBut4t2ux3bRlpzeyiWz9cZktSmMifsW84KSs3SOHDdLRVdk99ms5lPBWA0NElWQsl16qFQCTTYQItLK6pYjNZM9yvXIAWnQ3aqc/O1iPV63UcjabFYrmVElFsm5udziA2Pjo5QqVSws7PjF+OSP7c7f+kgtRApJFQEtgWfrQMbiO/7yOuq1SrW19cxm83w5MkTv4nSnTt3sLu7i729PQ/tCA8IbYihCUeSfASFKArl+L8mrWkaAweEEgHj8RjVahW3b99GtVr1h4sdHx/HyIhXJhiUJGxQjR4yf8EuMla8C1zd9oELhJUCpPPDFSvT6RRnZ2d+cYVN6aRl1WQiWvVqtYqNjQ1Uq1UPk8jtlstltFotNJtNb0ktHLFhc4rtwNCMxWs4WGkdma3H80CbzSZarRacc3jzzTexu7vrt44ge3F2doZmsxmjE61Tzna1jry2u0rI4isdHJLZbIZms4nhcIjt7W00Gg3s7+/7rUMIiejc2ixWlZVWcnY48SCtQBRFsYUPagFIMVlRpwi4XBBdLBZRq9VQLBb9wNHOpFjcS5hCnDsajbC1tYXbt29jMBh4heKyLstfE/frni38UZ8kJKyLVTy+N1Mb1tfXfWzg5OQE4/EYW1tbuHHjBra2tnyg6OTkBO12GycnJ2i1Wr6OhBk6oEIOb1I9eQ37MsnJt8KoJgfe9773PeRy85M/PvKRj6BaraLT6eDk5CSWb54kK63k7HSurrF7HwJXT/QNedpUTN5bLBaxsbGB3d1dNBoNRNF8ORyXp9kOUOtKS0nl4ep6QqL19XXs7+9jd3cXR0dHOD099XCFG+vQX7CYWelLncbT2ket+c7Ojsf9VD7CjPX1dezt7Xk4d3p66nE2I50MEvHAL+DSwdZMRKVRQxx9kqg/YrG7HQxcXEEqdjqd4uDgAO12G41Gw8/EOgsmyUopuXYyuXH13PVllN2gU8cV/6ro5GEJSbjCn89igxKrc6bQLRQ06qnhcX5P3H3//n2cnZ3h7t27eOutt/D2229jMpnEaDmudmm32x7qaOBG66+RTrYP68DgDNuDJz4XCgUfDeSsRt+j1+vh7OwstvsVZ0rCGi49U7rVOsI2pUDrtqh/2Se6YZDF8RT2MWcm55x3hLmhFNsvaYdcYEWUnNMek6Bms1ksusURHYocZrPzFd906mi9qByENpqtSLzJDiONpQ6bdq4OPKXKAMTya8jVf/vb38bDhw/x0Y9+FHfv3sXm5iY2Njb8gubHjx97R1eDLApZlIWwnc+gD+8pl8vY3t5GtVr1+SWkRgHg/Pwcx8fHsaPJqdhMeeDa01arFbPeanjs6iOFK5w91IcIzYT8XN/ZskgkHYi5WV9GtC30BF6B5W/AJSOgmJqWWa1GJnOZq12r1VCv15HJZPx59cS37Bj1vnWhMvEooYZCIIpaHk2j1RwVzhT8mxZxOBziy1/+Mr761a9iZ2cH9+7d8zkfe3t7uHXrlmcuaO31SEXWV7E/HWZy0JlMxq+A5yp3OpsA/LsxcEMajhFKjUxydzG13jpzUKmofCFGQ2GS/V8Hqg5mWmiNdPNv3bVAUwc4qytTkyZPe47nLwL46wCOLi77u1EUffHiu78D4OcATAH8rSiKfmeJZ8A5FwvMcMU6pzU2OjExO53TPgBPL9IC0iKqFdHlWTZ6aK/V+qmlURysP8S33B2KfPSTJ09wdnaGtbU1n+C0t7eHvb29mHPICCIzAemY2sgigyCNRgOVSgWj0QgHBwc4OjryacaFQgG1Ws0nMwHxKKqdHRbRl9oWen9Se11X7Cygba1L9vQ7/Z2m6E97jicA/KMoiv6+qejbAH4KwKcA7AP4Pefcx6MoSm05VpR4mZAjiZ6iUzQYDHzqJ0PDTATScjnqea9Ox6Hy0+rJQUiF0amY9acz6pzD9vY2KpUKOp0OTk9P0Wq1cHJygm9/+9uoVCpYX19HvV7H1tYWNjc3US6XUS6X/a66tHK0XgqvhsMhzs7O8OTJE58QRv9hOByi1WrFks8IufhjHWz9zXKWaZOktksyGvo9Yw/ApYXXZLSQY5uE4ZPkac/xTJLPAvh8FEVDAN91zr0D4EcB/NslnhNjFdR5tC+j+ItKwMAFcTsVQnMm9KxMXbNo68FyQ89nJxD/K3ZnUIj4EZhDDkZA1a/o9/u4f/8+7t+/71MOOIMxc48+CkPXDGgxjeHk5AQHBwfodDp+gAPwmYXM1qvVajEMq4NGMbEGkzTYZdsnFJcItd0iepF9RGvN2U8hiKY1KPEQmoWT5Fkw+S845/4qgD8G8N9EUXSG+ZmdfyjX8BzPK+LMEYdMxGGYlhtSch8PdXJIyZFPZQcOBgO/mkQTq4ifiac5MKQuwUCMCq1faFBQyfv9vl/GZR3lWq0G55xfBbO+vo6dnR0/y9AZJpVHhWTHauITEJ+eC4UCSqUSBoMBWq0W8vm8T/El/NG6qiW37wIgFgizUM5aXX6nym9nAEvHhp6p7ajKb/tA312t+4twPP8JgP8BQHTx+x8A+K+uU0AkRxzeunUrAuI7UgGXQQ2OZMIY3WBSHRPex0HB/4lp6cgQbmhjsiw6ZDoI+Fw6mXQGNcBBqMLnK1ancjCSyvQB+iDT6dRbbH0eByfv4bOp8Nls1uN/Uq31et1bYHL/dNBYdtJsxevItZME4LsqS8X+UjilWzfrZlDqT+lgUcee1+jqK03hVRqT7BJ9tkWw5amUPIqiA/7tnPunAP7Vxb9Ln+NpyotNnQBiG3JqwzKdFLh6jhAbJjTtAZf0IRsmhP10QKnQD7DOmzqdnEWY9QggZkmV9WEWXoiJ4P/qn6izSwXX80Z1L0nGFziAuQGQlq3QhO9RqVT8AOV9xPlsc86WdIzZjroXJQ2KQgrL9dv3ZF3Yx2qgbDl2kcQLseTu4qDai39/AsDXLv7+AoB/5pz7h5g7nh8D8O+WKdNOh+p82mmL+I3TOK2uxXW6Hwg7WqkqfVZo6rZKEaLNWD6tD60O4Qan7xBTYJOo9G/FxGSLgMsMSg5YptGqwxaCNBYf2/9pUDhbcABy5Q+dXG6vzNgEFz5oP+kMqbNjknNq66R5PXx3tp3OSNp/z6TkLnyO5190zv0w5nDlewD+5sXDvu6c+00A3wAwAfDzi5iVpBemZbZHafBldTW9NowqvlpAtRZs8FBIWf8PTetqAYH4SXL8nxgbuAwWqULbH75LqMPV8eLA01A2U34ZeVWLrW0ZYiRorflebBONJNOPYO49KVClcfV6IB5xpnVP8nXs55au1ZlGJYkwCMnTnuP5v6Vc/0sAfmlRuVaSPPGQhdVAADtK8aHCF20ktWxUCjtTWCWwYp1PC3V0EyF+RppMsxr5W5eM6bNVeWwUlvCAs9lgMIhZ9DSLaXFxaJ8WvgMVvNPp+NyYWq3mB7JmPeoqes5mOogW4eYkSRoc1ylvZSKewNUXsopJ66lWWp094DKqqbBEG9yeZmyfa3Gx/lZnk9dwoJD2JI1JDEsfgJFXpg9rGRpd1HdVqk/rx8HMbRyAS/6fVt62a+h97edqHKIo8glxjUYDjUbDY3SmuiqPr9tn0HJzFrUzpq1b0nc6a9o+s3ntSYMBWBElp8dsFYbcdjab9Ruzc+N3m2TF/G1VPrIhTN4io8EGIowglubgAOKZdhRaO84MrDPxK1cHUVkIJzQpzM4UZCWS2kUdNg30hLAqv2cCVsjPAeIn7CnjxHoz1K6YXFMQCEd0Owq2NdeYqjGxg0dnWOunkHrV7zi7sDwySQx0LVo4sRJKDsT3GdEpnR48O5YJRUDcCrDhSS9xOrecu8IYdXRCFJcV+5nNxtNglnLG6jBZsfy7Ym52oO56pdt06LOYs6IW1NJ1asFV4QHEnHIdXAD8jKHOKXNhgEs2SY2E/q2fJdGX2h40brqPow6s0D1pshJKTiZFhdZAFycDl8eCqFKocDsyruJngIXKYJ8bsnL8X7lZfq5Qg9bSrj3UcoDLEySSAknKp6tC6HVqAa3zy/u1zmmWjXWn9Wf7J+1folBBZwjOqlpvzqy6WxnFwiQ1Zvxbd8vlwLZ8vJbBdngRwaDnLlZZ1frQOdOV6iFrq+F90mDA5Wb7XHVvn6O/tT7W6lhcyM5Os/68L4nmopLpVE5njrQn83MY9lcruawkOaMsJzRg9L3sLMV4hYUTln8PWe5FMpvN/CIZUpFUeM7OizIPVVZGya2wAzTFVVkUIKyYhCYUNpAOEF5L65DkqScNCPt/yFlVsR2u9+smnZzNWNek93xewjZW+KYxCH6m0Vv+MDIJwHPmunroOhJ6P93kSJ+ZdH2arIyShxSWUUSLERcJsaM9UCvJ+qUpur3O0odJ9bfvEnoG8SYtJPEww/4M6zPMzvZICkylSSgWwHop1FBGSz9nH6gfw7bV9tVZaVG7LFJWdUqvqwMqK6HktLRKOREj6lZk2sgUCxlUaAVo3YlFLS2nUzPro7+BeDKQYkrbmZpkpPw838s6gbTemUwmlrvCzTPpRFMhk2CHtqW2h9bRQg5aRp3hqKh0mDUYpdFjVW7N6tT6sS7a1grdQgNB/9Ygl2ZQcvNQPWfolcTkjKCVy2VUKpWYcqlVVKeDayZp9ZjySqUhdtQlZ0mhYku9qbKoX2A5blUu+wwtk88k7tTF0fQldMpmuRpGt21nGRT+tooOXLIpCuNYZ5vmrM4uYw30f7ixfhqTY9tJ2zXk0/BvPRaRn/X7fe936SyYJiuh5NPp1OdEKD60569T2DFW0alUjOKVy2W/1US73Y6xMhSdhq2ELCSfp9O2Ci1VCNuqsinepRLodmgArjBOoXqxbnYgWuEzNI+eTqxy0pr+QEePDiYNTLPZjA0s3czT1jNppuPzQvXUwWadfu4kwLNR+Xzri6mshJJTtNPpaNogCpVAG5ZZa9rgmmNNbKuUVZJiX7e+VixsSvpbr7f3aGAqpLShciyMu25d+ZvtrSuK1AlOk0V1uE7dNAim2JyRVs4oQHxhd0hWQsnX1tZw48aNGH7VjELbeBYn6nZtbDBG9Hq9nse3SZ3wPNmLF8WEvIjybXuE2k/bjRAnSZGTZpLrOp8hxaYPwL9LpZKHLBb3W1kJJQcuR6Pusa25G9oYbGwgnl5L2MJpmHtd05LrKnfKokBCkoQcXfuZzj5J1/P7kCyr0NexnloPe59y5SEFZyBOoduydb7Ou2iKgs7s6rSSQdMUkCRZCSWfzWY4Pz/3L8bGtWc0Kj5TT1/hCAcEty1mp6oiJ2HHEDNgr0v6X1kN6+QlMTJJyklrmURRhu5Pg0lWlOVJwsz8jG26SJFsPUKDWutsHXv7TDJJVHplrZhrRKXn4EuSlVByTj+aeac0k/XGCVc4umnJOcWenp5eyR+xKaTaydaC8UdTYJW+U+sfGgi6YEA7XFMC9N1sKkGIqbChdW0jndXU+qpR0PL1XbUPWD/r9Gk9k3yFkNLqYNcBrpZZnXLew2eMx+OYP6D1Dw36JFkJJWeCkWUINDChYhkKa314DRsvtMKIz2FQg6JQJsSn2+fYv1lGyGpbbjjNmtl3CdVD8aqWaeukipQGVdLYEft+IbFtb8vSa7Qt0p6hUDVtFkybuVZCyaMoCoaDNZNOxVo2O9Vqh1HJ0wZM2lTHaxZBmUXfLYI2oRlLr9dyrsvg6PNtWaH6pilr2jPsu6UNYPt5qIyk54ZgWpo1XwklJzSwnyW9LCEKEE8y4t9cqKxWjs6KLS+JMWCdbLbjsg5U6B2TPgtZp5BFtnIda3adevHz6zi0y5b7vCTNH7GyMkquazaB5KkSuKQNaYWp3Ewa0mVZinlZrn12UlBCB0XI6bxORy66PjS9h6wur7XY9FkUMqm+q1ROkizTByuh5ACuKKNdQGGFg0AdOYUpFqemzQo2RZSiFj5pSn8aWabjk6bmpOc/bX2uO1ivU+6LlOvUeWWUnBy2nqVDHlT3MSwWiz7f2MIM8uzMX+GuVNyfJIS99RhzcvTq0aexKCGYYXNU+L0mQznnYgyGXfEfysm2M1vS4A0pl2Ul9IdsjLJRIafUOruWmeF1oSQ063TrO9ikO+uY27ZWY7isoq+EkivsYIM457xiKC4mxWitLJWEEEX3HFde1Yrma7AemhkYYiyWwef2GjtotHyFRaFMS4p16PRZIUVZRpT71sEUcmYtzWeZkiSIuWg2TJutQ9/r+74ycEUxpoUNqsC04KT++D1lNpuh2+363JbNzU0/MCwcomjZGtWzHROypEmdF4IYGpFl/a0fou+t96ZZ62UV2757ms8TUkbbDkmDMFSnNAaH19n2vK7ip8lKKHmaqKUjpajf6Whmqq0ug6MVJ4yxYtNu9cRh8uvLUmnLNLyFH9dhT1ZFtO6L6shrrPNvHWn9vex7p/koKiuv5HZ0JzUQr9UtJXgsIvfn47YWKqQj0yyNNv6LpsaS6qCyKsr/NPVYpg0XDZ7rPnfllRyIB3nS8DGVXHF2t9v1WymXy+UrZWsOBClI3YtFn5fm4C2j/CF8nyRPMy1/kJJU/7SBuYyT/CzPTpKVUHIqp1pU5a7txjRsMA0Kaeiaq4B46hszGq3jpAullREIYXc7mJJwq8Wr+kznnJ9Z7Pch5y3JwbQYWX8vkpCihcpIGoy2bSxtq5BSf1uWalkn3l6va1IVxqbJSig5gCuOpyYaUSFDKbHKStBp5G6rg8EAZ2dnMWeW6xZ5im8URTHqkMeycCcq3edDJQky8X+r6FF0uVqIAzOUbajsUpIyJyU3LSPaXmyTJEVPUh67e67WS58RYl9s+ywzI6oi26S9ZRa/rISS08LRCjsX3iqCVkIpRfLcvDaTyfgtjTUfnUpBy53L5bCxseGPRdGZgiviQ6xAGrOR1nEsL3RWkX1Hfb+QdU3LgV/kyFroZy2pDqqkZ2hOuSpgGmOzKKkrrc62f7ngWwOGaQN9JZQcSM/tCH0WorQ4SLies1qtIpfLod1u+xXejUYDtVoNpVIJmcz8aEQeIKXPI+ceWlb1NFgyNGDS5EVg8efhNCcNvKd5/jLMTEhCkfE0WWZ/8juYn/y2ByAC8KtRFP1j59wmgH8B4B7me5T/ZBRFZ25es38M4McB9AD8bBRFX1lYkwRJwr+h65hstbm5Ceccms2mP6b6ox/9KOr1OiaT+XHfXGnOlUQ8QcE55yFL0nOe9j1C8kGyNdeVZd41BCWSrgt9dt32VNy/bNstY8knmB989RXnXA3Al51zvwvgZwH8fhRFv+yc+xyAzwH42wB+DPMTJj4G4DOYny/0meu8iMW7ysmmOSjaaNxhdm1tzVv0VquF8/NzNJtNr+D1eh2bm5tYX1/3DiGDQno20bLsybKDUt/1eUpaec/CbCRx2AprFtF+lopdpg4WTtk06mVkmU34HwN4fPF32zn3TcxPdPss5idQAMCvA/gDzJX8swB+I5rX4A+dc+sufvzKwpexymL/txCF1xQKBX/eOhkVOqQHBwc4ODhAv99HoVDA9vY2tra2MJvND7w9PT2dN8hFDo11FLVBrZOsn4VgiQ2EqNO0yPotqwgWxtm2SitrGeub5JCGfJRQ3bQs26+h+0LOOz+3OUjP1fF08/M8/yMAXwKwJ4r7BHM4A8wHwH25jcccJio5G0L3/AgpNYAYM2HpJCold7FlBHM4HOLk5ASdTseH++v1OtrtNs7Ozvye3NVqFZlMBmdnZ2g2m8hkMh7DL3LINLlKj/TmdM5lZepAJbRx6v+2PUK/GScghEubjZLKSvIh7OdqWe1GTcsO0OvMPtbAhE7psLK0kjvnqgD+JYD/Ooqilnl45Jy71vznzDmeoWkvtGyLeSh2E37eMxwOcX5+DgDY3Nz0x36fnp6i0Wjgh3/4h1Gv1/0JxjyXvlKpoN/v4/DwEKVSyS+Q3tzcRLPZRL/fx2w2Q7PZRLPZRKvV8g4rT1ZeX19HrVbDxsYG6vW63xFLuXgNPOlWcBYOhKxoCDKEnLm02SHQD8Fy06g5O4MmWXa11NZo2TosKyxHF6w/F0vunMthruD/ZxRF/9fFxweEIc65mwAOLz5f6pjDKHCOZ4j3DWFAdgwtP1+ajmexWPTHfANzxb958ybefvttNBoNHB0dod1u+2NB8vk8Op0O+v0+dnZ2MBgMkM1mcePGDbRaLbTbbZyfn+PJkyf47ne/i/Pzc++gFgoF9Ho9HB8fo1AooF6vo1arYXt7G9vb22g0GsGthpkOHDr+JPTe9jM7CEKQLnRtSPRehRIhRbfU7iLWKE3RLbRaJEnszKIBvQy74jA/COubURT9Q/nqCwB+BsAvX/z+bfn8F5xzn8fc4WwuwuP2BZJGPCWU38y9vDOZ+XmUpVLJby+3v7+PO3fuIJvN4r333sOTJ0/Q6XRQr9exs7OD0WiE0WiEWq3mmZc33ngDzWYT//7f//vY0SNvvPGGhzu1Wg3lctlz6zx94ejoCO+99x6+853voNFoYHNzE5ubm6hWqyiXy/4U6aSV89eVp2EpQmWoRFF4D/RFz1nkZD9vR3sZWcaS/zkA/yWAP3XO/cnFZ38Xc+X+TefczwH4PoCfvPjui5jTh+9gTiH+tetUaBGTwemYAR6uw6Si8xDVSqWCcrmMXC6Hvb25u3D//n08fPgQ5+fn2Nrawptvvol8Po/Hjx/7AMNkMsG9e/cAAE+ePMH29jZGoxHOzs7wiU98Ant7e7HTFDhd0qozi3Fzc9PPAo8ePcLR0RE2NjZiCr8MnlxWnqUc2+ZUxpCSJ1n469bxWQb1dWUZduX/BZBUo78UuD4C8PPXrUgaZku63oaLqWDD4RDdbhedTgelUglnZ2cxPL2xsYE7d+6gUCjg8ePHaLVa2NzcxNramlfi+/fvo1QqYWdnB0dHR8jn87h165bfH4Z8uy6woNNXLpexvr6O3d1df1pyp9PB2dkZvv71r2MymaDRaGBnZwf7+/sx+vI67UV5HgMlRNWFLO+iPJFF8kEqN2VlIp4UuxGOZVqAeUMxHB9FkbfenU7HL5iYzWbY3NxEuVxGq9XCcDjE2toaPvGJT+Du3buYzWb4xje+gfPzc7z99tsALlfun56eotfroVQqodfrIZfL4caNG94Z5fOZ1Tgajfyeizxxgb5BuVzGdDpFvV7HxsYGzs7OcHBwgOPjYzx+/Bjvvfce7t69i7feegvVatXTnmtra7GT1XQ7PLt1HnExf/R7u1SNbcvfStlq+9qVVPZZujrLrpFlvyjrYmcty+qEaMW0gavPXHSIwkopeRJfav/WvBLboGQtyHJwS2hgzrbs7+97FmU6neLevXvIZDJot9uIoshb+2x2fp48g0lccKFUpq5FLRaLfjN60ojk6Km4m5ub2NnZwd27d9FqtdBsNr2yDwYD3L17F3t7e17BOZj4nrPZLLZXt22fUBuqUxq6JvS3Dpi0ZyxyHG1kMkRXWkmaoZJm+WVmsJVScuDqSy7DDCgvHEWRP4av3W6j2+36Te2dczg+Pka/30ev10OtVkM+n8fR0RGAuRUaDocoFAp+NmDUVAcY/QA+L5vNegZFLa3ul644N5fLYWdnBzs7O/jIRz6CVqvlLXyv18P6+jrq9bq/j2WQjUlTEMtiPE0yVxrzoeUnbeURKi9pH8WkPk56vv6fFoBTWTklX0aSXiqKIpRKJayvr2M0GuH8/NxPabSsnU4H2WwWjUYDAHB+fo5ut+uVmQrO4xF1z0XlaHUG0X0JNSikm1ayznYLjGKx6GeJg4MD3L9/H++99x5qtRru3buH7e1tH+CiZedA+iDa+XnJoln6RcorqeRJQgvLiCcwx8uFQgHlcjl2ggPTabm4gkpO/FgoFDws0gXH/CyUh6153rTwmsIbEl2Pytmg3W6j1WrhG9/4BjY2NrC3t+eV3R63/ipIiLP/IKnEV1LJk6wCV+tTcTOZjA/XM7zPxRAabWTaLWEG4Q1TbXU5HS2z5qmrVbdRWpbLhDC16AD8zq27u7vodDrodDrY3t5GJpPBkydPcH5+jk6ng1arha2tLZ8hmbR66Xkpz/NUwjQ48kHIyii5QhA2sOZ+UJQ6tA5SFF2m23I5HZe/kcpjcg+pQEY8GX5n2WRjGOKnMutCaYt3oyjya0R5LevLYwv5rryWjiSPZKxUKv6627dv49atW+h0Ojg+Psb5+bkPRPHAMPoObKPQBkohP4dGgQdNAZcOtD3iUJ1slmWT09IiodpnvNeyN8sMKovHtbxXApOHWBQ2lNJgXNyg+5co1mXkkwOEcEA39Od1URR5WpAsiDqP4/E4to2FHu+ijRxad8iZgmVyFy/Wi8rIvPXRaIR8Po9arYbpdOpTCbj44xOf+ITn5pmOwFmJz2TGpBoBhUocqOTtO50Oer2ef3c67dVqFfV6HaVSyW+gOpvN/DlMPISK0Ay4NDDaBjZZyzqKOgjSYMwyTNIroeS63tE6Z9bJsiNX4UPoJDDiY/5NpQJwZSEzhWwNFYBYmOfJs+O5wp/UIS2hpuiqVaei0QdgvRuNho+Cdjodr6iHh4d48OABqtUqbt68iTt37ni6st1uA4A/Pqbb7fqc+fF47COwPHJdYwp0qPf29rC+vo58Pu9P5+j3+2i1Wjg9PfXpC5lMBoPBwFOthINsN/o115Fl4VWIdrR+SdoKoZVRcuBqTkra6ExiV9LWTybdR9HBpakD1WoVURSh3+9jOBz6hc96shzZGruGMwSrbH04o5DVoZWu1WpoNBo4PDzEwcEB3nvvPZycnGB9fR29Xi/mYxD3DwYDP9Bns/mOY2SZqtUqGo0Gbt686ReS0FI759BoNLC/v+99lsFggGazicFgEIN+fK9l+fKQvHY8n0GSol+hyB/FwiP+PZlM/CqiUqnk6TuyMsTVtNSEMdaqhDAon8EBQ8XO5/OeUWm1Wuj3+9jY2MDOzg4ODw99PgytN5fxTadTVKtV3L59288QVGymOrRaLfR6Pb+99fr6ulf0ZrOJBw8e+MHFXJzbt28DgLf+hH9JftEqykoq+XUbTRs7tL8gcBXyhO5X60TowZ23ptOpx6i0boQDtPJc/Kw4XqN2oSlVlYbPuHHjBvb29jwsGY/H2N3dxc7ODh4+fIiTkxMUi0Xs7e351eu5XM4zOFEUodVq+UBYrVbzue6MC/B7phusr697mMJB0+v1cHZ2hnq9jnq9jkwmg2636w/V1fZLk7QZ7IOQlVByjaLx57p0WAjL63dAuqIrPAHmyV6lUsmfFzkcDvH48WM8efIEJycnyGaz2N/fx40bNzycIVsTCkfbnXj5zF6v560rcf3x8TF6vR4+8pGPoNFoeBiytraGra0tfy0XbHAB9sbGhj+xeDAYeIXlyqhKpYJ79+7BOYdut4t2u41er4fHjx/7I96J3Tl4ptMpWq0Wjo+PMZvN/J6SlhkJ+TX6vR0Uz0PBly1jpZSc4XOlpJbhWJVFSFJkPRPSlkHYoRmFALwydTodnJ+f+zyYer3uWZHhcOijlkq96X7kehQjaUpuZFSv173TeHZ2hsPDQ3zta1/z2ZFvvvmmhyt09hjw4uDLZrMee0dR5DMgt7a2cHZ2hk6n4/2MZrPp37NUKqHRaPj6KgXa6/XQ7/c9bOEmqGRNdJEK3zPt5GY1PnqETcgohfrHfq5GY1FwbCWUnFaDopsBLTta06ZPS/np9YQl1WrVO5bAfA/FR48e4f79+2i326jVarhz5w4ajYZPlCJMIY4lfamDk53BgchOLRQK/rmVSgW9Xs8/t91u++eenJxgf38fGxsbaDQa2N3d9SnF3W7XK60aiOPjY7Tbbc/x53I5nympVpROaqlUQqFQ8IOvVqthPB77JX9qHHSGsoaDfRfqG7vKXtMkQn1sP0uiFnXmT5KVUHJyvLTGi4IET4MB7X36N50qcsxPnjzBu+++i3a7jc3NTbz99ts+CqkWTi2+4lR1MtVi8X8GYcjEEHfn83ncu3cPN27cwPvvv49Hjx7h61//OprNJu7evYt+v++x9WQyQbfb9fifvHw+n/eLOaiAutUGcLkYnAGy8XjsqVFy+ixXN0DVIBIQzi1Pgit2Vl7kc4X6KqTMyzA7K6HkwKUyaA55Elx53sIACwCcnp7i8PAQuVwOn/70p3H37l2/0JoZjI1GA5VKxS/OGI1GaLfbmM1mXvltXjyVjZQfcXg+n8f29rbPTxmNRmg2mx6+PXz4EN/73vcwGAywvb2Ner2Ora0tPxvwN2cgDXQ557yyM/2X5QLwM1Kn08F4PPZRV918lWkJzyKhIE6aBU7ixe339u8kWQklJ7+tyUwM4LxoJacCrq2t4fz83K/qv3fvnt+X5ejoyCd1kcFhshTTAdSSaifxffR7TtWTyQQnJyd+d4E33njDb4tBx7dcLuPx48extITJZILt7W2Uy2X/OZVT9ylkoMo55yOuAGLsD9OLOZMB8Zlt2bjFMpLmnCZJKBJ6XWZmZZScU6LNBXmRNJNaWYaz33rrLezs7KBWq3lL1+v1cHBw4FcbAfD7K1KpCA2oaHwPHQQMqJTL5djJF/fv38eXvvQlDAYDvP3229ja2vIYOZPJYGNjw1tbZlN2Op0YrifsYNRX0yA4q6hzxoHN8iaTiT+0gMElvs91Wa6QLIOdQ/fYvy1rtYyshJJTyB4AiMEWIH0KW4TLknCc5n4D8A5kLpfzDp0uh9vc3MTNmzfhnPMrixhsGY/HaLfbfl0pV/KTXaEl52Di+9Lp+/a3v40vf/nLGA6H+OQnPwnn5oujCYsePXqEg4MDz3sXi0WMRiPvH9RqNdRqNe8fEH6sr6/79uS7ap4PgBiOZwoyszH1kAIqPvdYJz7noE6Cl2qs1NlU47DoHr3XKvwiWLsySh5SVMs3h17G3nddq6NQiVM3FWIwGODwcL6dzJtvvont7W0A8Os+7Yb6LHM8HvskKottyaJwQ6LZbOYHw7e+9S185StfwdnZGT71qU/57xkkqlarODw8RLvdxvb2tv+erAmtPyEN/QDOWBTLmGj7En+zbUJwxZID15EkCnfRPaHo6rIzw0oouTaepaCexiNPopvsdfybtJ8utp1MJn6HrL29Pdy4cQOlUgmdTscrcKFQiDmdXNhMiELFI04nRmbOu67nvHv3LqrVKr72ta/h+9//PvL5PD72sY95fppWOZvN4ujoCM1mE2tra9jY2EA2m/XOKteEUmzqrW1fDV5pSjEQX1SutB9hnG3bpP5J67ckmthid9t/+sxFerESSg5cb2Q/jSSVSYtMn4ABFoav7969i/39fUwmExwfH6PT6WAymfgEKCoycIl5FdOSliNfDcxnCAZuuAfLcDhEo9HApz71Kb8x6cHBAXZ2djxWzmTmGycVi0U8fvwYJycnPvjDvPLrYFXCFg2mAeFZcxlFTuu3JEjytGLrt/I8uZWngR7P8hwuU+MzSaHt7e1hd3fXJ2QxKklOmWwLNzEiliUu1vor/9xut9Fut3F4eIjZbIb9/X0PfXZ2drC3t4eDgwMcHR1hOp366CWhyebmJqbTKQ4PD/02HMwqTEs5TWuHF93Wz1NCA+aVwORAOum/jCTh9jSh5WbKrB6LWK1WMRqNPGzp9Xp+YQM5aIazac11VRBnB4bRdVW/cw6dTgcnJycYj8c+rxuYU3h8Nv2CXq/nl/Fls1kPU05PT31QSKPGy4hGlkNOvv6d1rZPw748L2r4lcHkGvihx664kNO+WtukcpK+DwUkgEtMSHqPVp3MA5eHMY+FCwwGg4HP/uMKIHXcyFsrDOIqnFwu5/NfmEpwcHCA9fV1v08iBxkAb/VHoxG2t7dj9wPA2dkZRqNRYgamtiPrSIx9HT8oqQ115l3UN7w+hPvttSEmTf0DOtaLZq+VUHLgMp9B/6eQxiJseBpcmERJqfWuVCqIonk+iiYtOTdfSUTF5265N2/e9CF2zevWoBA5cmA+YPr9vg/yMLeEfHu32/WDgFhe11wCc4Xf2Njw1COdUjqCSccn8jsdiPwuZKGT2BeWa/G/ZWNC7c9ytMwkpiTJUOkzOCOSz0+SlVByteD6GXCZ6xG6J/RZ0rZoaakCtGyEH6T7yBPz/kql4vdY3NjYiD2PCVssTze/16zKKIp8eq1zl4sm6JRq9JQr84nzmUfSbrcxHo89j14sFn3uuHa2vifpUf0uTakW5d+HJI3pSII6SVY4CRZpUInGYxFkWQklXyRW+a+LvZeZcoFLtkH3HSTLwh2tRqORX8DAIBADKRoYUaXW/60Mh8PERCcqPweSpgNwbxkOXp0xlmmH6wrb+2kd1OeFwW1Zy9RrpZXcBiPs/6Hr0zp6UUNzAOlUzUANVwmtr69jf3/fb9yvK2mUc6cCpqUL22ssJHDO+XWf5L/pyDLJixSo0pZJ7/Us8jyV9FnF8uSLZKWVHLh+MCikVFTcZcqxeJP5IYxUbm5uegvPfBHdAsPue2jxI5/BeiTBMXWwCJ90QOiiBT7bPpPC2ckq+yKrvEqKraLtuIyyP8s5nr8I4K8DOLq49O9GUfTFi3v+DoCfAzAF8LeiKPqdtGdYbKjYy36f1klpzo82SGgQAJc4lOxKNpv1WXyz2fw4ROccnjx54qOeVCCtNweUZvFRWdPwrP2fz9UduKj4ZG+0kxcNXtsOiySpHZe5Z5lnpJW1zLssUx/g2c7xBIB/FEXR3zeVexvATwH4FIB9AL/nnPt4FEWpSclsfFonprMCl86nbhehzId2dGgJllo+O/Lt8ivCnclk4jf/pINJpxHAlU2J+AwuaFZcT05dnTlr7XUrOr4rgzu8j0vumDLARR6cSfjcpPbVhQ+mz4L3WKNg29uKhV22b/S9+bfuKqY/ScLvlPoM7YSg8izneCbJZwF8PoqiIYDvOufeAfCjAP5t2nNYeVWMi2fGcDL/Dx0HLnUOls+GCA2CkHXQVFq7dRqAWPadZvaxPF3LqLDEzkw2R4QKadkQps0q9FBlT3r3NOu6rMVM+yxUXmiAhK616b/LioWgz83xdPFzPP8c5gdg/VUAf4y5tT/DfAD8odzGczxtWbEjDlWBbYAgNLpD6w7T4IptFJUQfFF8z84gfNBccbVYVED9PqTk+g60+PrspHqlpbMukueBr5dx3K9b3iKrnTRAQ4YqSZbOl3TmHE/MjxN/C8APY27p/8GyZV1U6lejKPqRKIp+pFKp+Ion4WZTlysO6fNgENJE87B1erRWOWkwMZdcf3QjzUWiMIDrQu2Prq5KG/BWLFRYJYczVB/rty2q81Of4xlF0YF8/08B/KuLf5c6x3OJZz4VJ/s8O8haVdYpbYZJkkW4MU2s9U5aaHCd2epVkNCs9TQz2VOf4+kuDqq9+PcnAHzt4u8vAPhnzrl/iLnj+TEA/27Rc2zl0zrlg6C+FD4B8ZVKFv/yd8ixBOKzgJWkiJ8dTPo3o7L2+iQ68lWWEFy57oB9lnM8f9o598OY04rfA/A3LyrwdefcbwL4BubMzM8vYlZYcYaz1fHU5XBJ94VC9km/0+7n/xRa6rW1NZ+7oolXqmiKw3kvoYg6vAppLOOgP2mMhzqdoYFg30PvDUGYJKW5DusSKk/rluT0A/HUDaVak+qV5kiH5FnO8fxiyj2/BOCXlqqBSKjSaZbJKrgtZ5GC22db5bJOsEYodQmYWnZVfLuvun6eNBUvUnRlX64jTzO7pdXheZXFuoX6KvR3qJxF77YyEU+1fIs6MMSGJCn0Mth+EVZmfbh20tbDwhI7tVrF1RPc0maYZSWJhUiy7Ms4909TD1t+qB2SruXA188XzUzL1nFllPy6kqYkT6Poy8qiKTxtgFrlehbfIc2aKfOQJMuyLte9N83yp1nzUJvYfnva9npllTyEL5+HVbTPWCRJzqm9X4NZy5a97HOfpt7XsebPS0ID0X6edo+VZZmWV1bJgTCOVklyyp6HhJQ1KaU2tADhWUXfPQ2qhHD/snVI8huuc891ndrryLL9uhJKrg6bzSJMU2BlGuy1OkUmsQD6DFtWSLSOIbxJJibtPZOs17L4U+/V30nski1zWadtUZstuue6zqG+Q1qOjUZ9aTzS0pmBFVFyiq7jXEY4OGyDWAVfRJnpGZ1anj3NjD/KuOhmPMxdsZ2jSVQsg8EhTcCydXtaCi/UTkllXPe+tHuf5jn2fe0OuqGBPJvNYgedpdGNwAopueaELDPqQ8EQ/a3lLJoNQnWwdVsk7IS0g7nsFniLJKnjFtGqy36+TPskfb5IoZcZgKHZMCloRrF7xfCzNFkJJX9afLYMXgw5WJZ9eB748IOS0DsuQw+m4fbr+itPw64s8/l1neFlr18JJX9aWbYxFnXmsl76qski5zLtHsrzfu9F5S8Lx64zkBa9wyur5Gl87DL3PS8q72WJrf8ip/N5cc7L3Kt1sgYmzfo+jbFZ5p6VUHJtjEWsQKjD9Dr9P2nEE6os88wQ6/Es8Mri2edR1jLTdpJzHqrHMuWl+T62nazSq09l38OWG2ovLT90jZWVUHIAfo8TbrfGivM4EG5Yz9Uy9K65SIH7EpZKJb+indl69MbT0lDpDNLD16V2yoBY+kr/TmtsdjR3tuV+i7ZOi5woPk9XJLHO2i7KOnApHeuo76nLCG1yHMuxz+f3uqmptpd9vr47gNgRibqUURem6NEzTNzj9XwnlsN8+iRZCSWPoih2hLaOVnYI1zWGtjSLosif5KALfnW7Nz4n9Gw+p91u+/0KWa7SfdaCWEu1SNG1c6xFC71TklhLCMRpUL2OQtbHzky6lM5ae1uGfVe74COJr7YsSBRdpV7tfd1u1+/8y4MRdB0rdYZ9nyYroeSFQgE/9EM/dIUj1RfiWkvuNMVGojXhBvS0/LqCfpF1DEEbtexJXG1IQopvn8P6cA1oGmSxmFbrpUlNugZVFZYKlLZVB61naJ+YpLppSmwo0huiBzmbqMHS2IKm204mE3+EOrfk0DoD8Fvj2T3Zrbjr0kcvQpxzRwC6AI5fdl0SZBurWzdgtev3QdbtbhRFO/bDlVByAHDO/XEURT/ysusRklWuG7Da9VuFuj3dosPX8lpeIXmt5K/lQy+rpOS/+rIrkCKrXDdgtev30uu2Mpj8tbyWFyWrZMlfy2t5IfLSldw591ecc99yzr3jnPvcy64PADjnvuec+1Pn3J845/744rNN59zvOue+c/F74wOqy6855w6dc1+Tz4J1cXP5lYu2/A/OuU+/pPr9onPu4UX7/Ylz7sflu79zUb9vOef+0xddPwDx8OsH/QMgC+BdAG8CyAP4KoC3X2adLur1PQDb5rP/CcDnLv7+HID/8QOqy18A8GkAX1tUFwA/DuD/BuAA/FkAX3pJ9ftFAP9t4Nq3L/q4AOCNi77Pvug6vmxL/qMA3omi6L0oikYAPo/5rrirKJ8F8OsXf/86gP/8g3hoFEX/BsDpknX5LIDfiObyhwDWnXM3X0L9kuSzuNjxOIqi7wLgjscvVF62kt8CcF/+D+6A+xIkAvCvnXNfdvPddwFgL7rcFu8J5ocSvCxJqssqtecvXECmXxNo91Lq97KVfFXlz0dR9GkAPwbg551zf0G/jOZz70rQUqtUF5Fn2vH4ecvLVvLnsgPu85Yoih5e/D4E8FuYT6kHnPovfh++vBom1mUl2jOKooMoiqZRFM0A/FNcQpKXUr+XreR/BOBjzrk3nHN5zI9h+cLLrJBzruLmx8bAOVcB8Jcx37H3CwB+5uKynwHw2y+nhkBKXb4A4K9esCx/FkBTYM0HJsYP+AnEdzz+KedcwTn3Bpbc8fiZ5YNgCBZ45z8O4NuYe9p/bwXq8ybmDMBXAXyddQKwBeD3AXwHwO8B2PyA6vPPMZ/yx5hj2J9LqgvmrMr/ctGWfwrgR15S/f6Pi+f/B8wV+6Zc//cu6vctAD/2QbTh64jna/nQy8uGK6/ltbxwea3kr+VDL6+V/LV86OW1kr+WD728VvLX8qGX10r+Wj708lrJX8uHXl4r+Wv50Mv/D6NILjOmhOXDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Hy = np.array([[ -1,-2, -1],[0,0,0],[1,2,1]],dtype=np.float32)\n",
    "Gy = convolve2d(gray,Hy,mode='same')\n",
    "print(f\"Image after convolving with Vertical Sobel Filter\\n\")\n",
    "plt.imshow(Gy,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image after combining outputs from both Horizontal and Vertical Sobel Filters\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5055c19ca0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = (Gx*Gx + Gy*Gy)**0.5\n",
    "print(f'Image after combining outputs from both Horizontal and Vertical Sobel Filters')\n",
    "plt.imshow(G,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Listing 3-6 Convolutional Neural Network for Digit Recognition on the MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version 2.4.1\n",
      "(60000, 28, 28, 1) (60000,)\n",
      "WARNING:tensorflow:AutoGraph could not transform <__main__.conv_model object at 0x7fe62c698eb0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <__main__.conv_model object at 0x7fe62c698eb0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <bound method conv_model.call of <__main__.conv_model object at 0x7fe62c698eb0>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <bound method conv_model.call of <__main__.conv_model object at 0x7fe62c698eb0>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Epoch 1 : loss: 0.2697 ,accuracy:0.9228\n",
      "\n",
      "Epoch 2 : loss: 0.0514 ,accuracy:0.9827\n",
      "\n",
      "Epoch 3 : loss: 0.037 ,accuracy:0.9873\n",
      "\n",
      "Epoch 4 : loss: 0.0341 ,accuracy:0.9871\n",
      "\n",
      "Epoch 5 : loss: 0.0289 ,accuracy:0.9889\n",
      "\n",
      "Epoch 6 : loss: 0.0281 ,accuracy:0.9894\n",
      "\n",
      "Epoch 7 : loss: 0.0272 ,accuracy:0.9897\n",
      "\n",
      "Epoch 8 : loss: 0.0223 ,accuracy:0.9915\n",
      "\n",
      "Epoch 9 : loss: 0.0265 ,accuracy:0.9902\n",
      "\n",
      "Epoch 10 : loss: 0.0206 ,accuracy:0.9922\n",
      "\n",
      "Epoch 11 : loss: 0.0204 ,accuracy:0.9922\n",
      "\n",
      "Epoch 12 : loss: 0.0207 ,accuracy:0.9923\n",
      "\n",
      "Epoch 13 : loss: 0.0193 ,accuracy:0.9931\n",
      "\n",
      "Epoch 14 : loss: 0.0278 ,accuracy:0.9908\n",
      "\n",
      "Epoch 15 : loss: 0.0232 ,accuracy:0.9922\n",
      "\n",
      "Epoch 16 : loss: 0.018 ,accuracy:0.9932\n",
      "\n",
      "Epoch 17 : loss: 0.0205 ,accuracy:0.9928\n",
      "\n",
      "Epoch 18 : loss: 0.016 ,accuracy:0.9938\n",
      "\n",
      "Epoch 19 : loss: 0.023 ,accuracy:0.9925\n",
      "\n",
      "Epoch 20 : loss: 0.0245 ,accuracy:0.9924\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-21 22:50:33.085229: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.65GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-07-21 22:50:33.317913: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.76GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results on Test Dataset: loss: 0.0705 accuracy: 0.9867\n",
      "7\n",
      "2\n",
      "1\n",
      "0\n",
      "4\n",
      "1\n",
      "4\n",
      "9\n",
      "5\n",
      "9\n",
      "Total processing time: 80.2550847530365 secs\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print('tensorflow version', tf.__version__)\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from tensorflow.keras import Model, layers\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "\n",
    "\n",
    "# Function to Read the MNIST dataset along with the labels\n",
    "\n",
    "def read_infile():\n",
    "    (train_X, train_Y), (test_X, test_Y) = tf.keras.datasets.mnist.load_data()\n",
    "    train_X,test_X = np.expand_dims(train_X, -1), np.expand_dims(test_X,-1)\n",
    "    #print(train_X.shape,test_X.shape)\n",
    "    return train_X, train_Y,test_X, test_Y\n",
    "\n",
    "def normalize(train_X):\n",
    "    return train_X/255.0\n",
    "\n",
    "\n",
    "\n",
    "\"\"\" C O N V O L U T I O N  L A Y E R \"\"\"\n",
    "\n",
    "def conv2d(num_filters,kernel_size=3,strides=1,padding='SAME',activation='relu'):\n",
    "    conv_layer = layers.Conv2D(num_filters,kernel_size,strides=(strides,strides),padding=padding,activation='relu')\n",
    "    return conv_layer\n",
    "    \n",
    "\n",
    "\"\"\" P O O L I N G L A Y E R \"\"\"\n",
    "\n",
    "def maxpool2d(ksize=2,strides=2,padding='SAME'):\n",
    "    return layers.MaxPool2D(pool_size=(ksize, ksize),strides=strides,padding=padding)\n",
    "\n",
    "# Convolution Model class \n",
    "class conv_model(Model):\n",
    "    # Set layers.\n",
    "    def __init__(self,input_size=28,filters=[64,128],fc_units=1024,kernel_size=3,strides=1,padding='SAME',ksize=2,n_classes=10):\n",
    "        super(conv_model, self).__init__()\n",
    "        self.conv1 = conv2d(num_filters=filters[0],kernel_size=kernel_size,strides=strides,padding=padding,activation='relu')\n",
    "        self.conv2 = conv2d(num_filters=filters[1],kernel_size=kernel_size,strides=strides,padding=padding,activation='relu')\n",
    "        self.maxpool1 = maxpool2d(ksize=ksize,strides=ksize,padding='SAME')\n",
    "        self.maxpool2 = maxpool2d(ksize=ksize,strides=ksize,padding='SAME')\n",
    "        self.fc = layers.Dense(fc_units,activation='relu')\n",
    "        self.out = layers.Dense(n_classes)\n",
    " \n",
    "    # Set forward pass.\n",
    "    def call(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.maxpool2(x)\n",
    "        x = tf.reshape(x,(tf.shape(x)[0],-1))\n",
    "        x = self.fc(x)\n",
    "        x = self.out(x)\n",
    "        return x \n",
    "\n",
    "            \n",
    "# Define the Categorical Cross Entropy that does a softmax on the final layer output logits\n",
    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.SUM)\n",
    "\n",
    "#Learning rate\n",
    "learning_rate = 0.01\n",
    "# Define optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate)  \n",
    "\n",
    "\n",
    "X_train, y_train, X_test, y_test = read_infile()\n",
    "X_train, X_test = normalize(X_train), normalize(X_test)\n",
    "num_train_recs, num_test_recs = X_train.shape[0], X_test.shape[0]\n",
    "print(X_train.shape,y_train.shape)\n",
    "# build the model\n",
    "model = conv_model(input_size=28,filters=[64,128],fc_units=1024,kernel_size=3,strides=1,padding='SAME',ksize=2,n_classes=10)\n",
    "#print(model.summary())\n",
    "model_graph = tf.function(model)\n",
    "\n",
    "epochs = 20\n",
    "batch_size = 256\n",
    "loss_trace = []\n",
    "accuracy_trace = []\n",
    "\n",
    "\n",
    "num_train_recs,num_test_recs = X_train.shape[0], X_test.shape[0]\n",
    "num_batches = num_train_recs // batch_size\n",
    "order_ = np.arange(num_train_recs)\n",
    "\n",
    "\n",
    "start_time = time.time()\n",
    "for i in range(epochs):\n",
    "    loss, accuracy = 0,0\n",
    "    np.random.shuffle(order_)\n",
    "    X_train,y_train = X_train[order_], y_train[order_]\n",
    "    for j in range(num_batches):\n",
    "        X_train_batch = tf.constant(X_train[j*batch_size:(j+1)*batch_size],dtype=tf.float32)\n",
    "        y_train_batch = tf.constant(y_train[j*batch_size:(j+1)*batch_size])  \n",
    "        #print(X_train_batch,y_train_batch)\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred_batch = model_graph(X_train_batch)\n",
    "            loss_ = loss_fn(y_train_batch,y_pred_batch)\n",
    "            \n",
    "\n",
    "            # compute gradient\n",
    "        gradients = tape.gradient(loss_, model.trainable_variables)\n",
    "            # update the parameters\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "        accuracy += np.sum(y_train_batch.numpy() == np.argmax(y_pred_batch.numpy(),axis=1))\n",
    "        loss += loss_.numpy()\n",
    "    loss /= num_train_recs\n",
    "    accuracy /= num_train_recs\n",
    "    loss_trace.append(loss)\n",
    "    accuracy_trace.append(accuracy)\n",
    "    print(f\"Epoch {i+1} : loss: {np.round(loss,4)} ,accuracy:{np.round(accuracy,4)}\\n\")\n",
    "                            \n",
    "X_test, y_test = tf.constant(X_test), tf.constant(y_test)\n",
    "y_pred_test = model(X_test)\n",
    "loss_test = loss_fn(y_test,y_pred_test).numpy()/num_test_recs\n",
    "accuracy_test = np.mean(y_test.numpy() == np.argmax(y_pred_test.numpy(),axis=1))\n",
    "print('Results on Test Dataset:','loss:',np.round(loss_test,4),'accuracy:',np.round(accuracy_test,4))  \n",
    "f, a = plt.subplots(1, 10, figsize=(10, 2))\n",
    "for i in range(10):\n",
    "        a[i].imshow(X_test.numpy()[i,:])\n",
    "        print(y_test.numpy()[i])\n",
    "print(f\"Total processing time: {time.time() - start_time} secs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-7 Real World use of Convolutional Neural Network\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    Load the relevant libraries\n",
    "    Download the data from https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening\n",
    "\"\"\"\n",
    "import glob\n",
    "import cv2\n",
    "import time\n",
    "import os\n",
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "print('tensorflow version', tf.__version__)\n",
    "import numpy as np\n",
    "from tensorflow.keras import Model, layers\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from elapsedtimer import ElapsedTimer\n",
    "import pandas as pd\n",
    "\n",
    "## Create the different layer functions\n",
    "\n",
    "\"\"\" C O N V O L U T I O N  L A Y E R \"\"\"\n",
    "\n",
    "def conv2d(num_filters, kernel_size=3, strides=1, padding='SAME', activation='relu'):\n",
    "    conv_layer = layers.Conv2D(num_filters, kernel_size, strides=(strides, strides), padding=padding, activation='relu')\n",
    "    return conv_layer\n",
    "\n",
    "\n",
    "\"\"\" P O O L I N G  L A Y E R \"\"\"\n",
    "\n",
    "def maxpool2d(ksize=2, strides=2, padding='SAME'):\n",
    "    return layers.MaxPool2D(pool_size=(ksize, ksize), strides=strides, padding=padding)\n",
    "\n",
    "\n",
    "# Convolution Model class \n",
    "class conv_model(Model):\n",
    "    # Set layers.\n",
    "    def __init__(self, filters=[64, 128,256], fc_units=[512,512],\n",
    "                 kernel_size=3, strides=1, padding='SAME',\n",
    "                 ksize=2, n_classes=3,dropout=0.5):\n",
    "        \n",
    "        super(conv_model, self).__init__()\n",
    "        self.conv1 = conv2d(num_filters=filters[0], kernel_size=kernel_size, strides=strides, padding=padding,\n",
    "                            activation='relu')\n",
    "        self.conv2 = conv2d(num_filters=filters[1], kernel_size=kernel_size, strides=strides, padding=padding,\n",
    "                            activation='relu')\n",
    "        self.conv3 = conv2d(num_filters=filters[2], kernel_size=kernel_size, strides=strides, padding=padding,\n",
    "                            activation='relu')\n",
    "\n",
    "        self.maxpool1 = maxpool2d(ksize=ksize, strides=ksize, padding='SAME')\n",
    "        self.maxpool2 = maxpool2d(ksize=ksize, strides=ksize, padding='SAME')\n",
    "        self.maxpool3 = maxpool2d(ksize=ksize, strides=ksize, padding='SAME')\n",
    "        self.fc1 = layers.Dense(fc_units[0], activation='relu')\n",
    "        self.fc2 = layers.Dense(fc_units[1], activation='relu')\n",
    "        self.out = layers.Dense(n_classes)\n",
    "        self.dropout1 = layers.Dropout(rate=dropout)\n",
    "        self.dropout2 = layers.Dropout(rate=dropout)\n",
    "\n",
    "    # Set forward pass.\n",
    "    def call(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.maxpool2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.maxpool3(x)\n",
    "        x = tf.reshape(x, (tf.shape(x)[0], -1))\n",
    "        x = self.fc1(x)\n",
    "        x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = self.out(x)\n",
    "        probs = tf.nn.softmax(x,axis=1)\n",
    "        return x,probs\n",
    "\n",
    "\n",
    "# Utility to read an image to numpy\n",
    "def get_im_cv2(path,input_size):\n",
    "    \"\"\"\n",
    "    :param path: Image path\n",
    "    :return: np.ndarray\n",
    "    \"\"\"\n",
    "    img = cv2.imread(path)\n",
    "    resized = cv2.resize(img, (input_size, input_size), cv2.INTER_LINEAR)\n",
    "    return resized\n",
    "\n",
    "# Utility to process all training images and store as numpy arrays\n",
    "def load_train(path,input_size):\n",
    "    \"\"\"\n",
    "    :param path: Training images path\n",
    "    :return: train and test labels in np.array\n",
    "    \"\"\"\n",
    "    assert Path(path).exists()\n",
    "    \n",
    "    X_train = []\n",
    "    X_train_id = []\n",
    "    y_train = []\n",
    "    start_time = time.time()\n",
    "\n",
    "    folders = ['Type_1', 'Type_2', 'Type_3']\n",
    "    for fld in folders:\n",
    "        index = folders.index(fld)\n",
    "        path_glob = f\"{Path(path)}/train/{fld}/*.jpg\"\n",
    "        files = glob.glob(path_glob)\n",
    "\n",
    "        for fl in files:\n",
    "            flbase = os.path.basename(fl)\n",
    "            img = get_im_cv2(fl,input_size=input_size)\n",
    "            X_train.append(img)\n",
    "            X_train_id.append(flbase)\n",
    "            y_train.append(index)\n",
    "\n",
    "    for fld in folders:\n",
    "        index = folders.index(fld)\n",
    "        path_glob = f\"{Path(path)}/Additional/{fld}/*.jpg\"\n",
    "        files = glob.glob(path_glob)\n",
    "\n",
    "        for fl in files:\n",
    "            flbase = os.path.basename(fl)\n",
    "            # print fl\n",
    "            img = get_im_cv2(fl,input_size=input_size)\n",
    "            X_train.append(img)\n",
    "            X_train_id.append(flbase)\n",
    "            y_train.append(index)\n",
    "    return X_train,  y_train,  X_train_id\n",
    "\n",
    "# Utility to process all training images and store as numpy arrays\n",
    "def load_test(path,input_size):\n",
    "\n",
    "    path_glob = os.path.join(f'{Path(path)}/test/*.jpg')\n",
    "    files = sorted(glob.glob(path_glob))\n",
    "\n",
    "    X_test = []\n",
    "    X_test_id = []\n",
    "    for fl in files:\n",
    "        flbase = os.path.basename(fl)\n",
    "        img = get_im_cv2(fl,input_size)\n",
    "        X_test.append(img)\n",
    "        X_test_id.append(flbase)\n",
    "    path_glob = os.path.join(f'{Path(path)}/test_stg2/*.jpg')\n",
    "    files = sorted(glob.glob(path_glob))\n",
    "    for fl in files:\n",
    "        flbase = os.path.basename(fl)\n",
    "        img = get_im_cv2(fl,input_size)\n",
    "        X_test.append(img)\n",
    "    return X_test, X_test_id\n",
    "\n",
    "def read_and_normalize_train_data(train_path,input_size):\n",
    "    train_data, train_target, train_id = load_train(train_path,input_size)\n",
    "\n",
    "    print('Convert to numpy...')\n",
    "    train_data = np.array(train_data, dtype=np.uint8)\n",
    "    train_target = np.array(train_target, dtype=np.uint8)\n",
    "\n",
    "    \n",
    "    print('Reshape...')\n",
    "    train_data = train_data.transpose((0, 2, 3, 1))\n",
    "    train_data = train_data.transpose((0, 1, 3, 2))\n",
    "\n",
    "    print('Convert to float...')\n",
    "    train_data = train_data.astype('float32')\n",
    "    train_data = train_data / 255\n",
    "    #train_target = np_utils.to_categorical(train_target, 3)\n",
    "    print('Train shape:', train_data.shape)\n",
    "    print(train_data.shape[0], 'train samples')\n",
    "    return train_data, train_target, train_id\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def read_and_normalize_test_data(test_path,input_size):\n",
    "    \n",
    "    test_data, test_id = load_test(test_path,input_size)\n",
    "\n",
    "    test_data = np.array(test_data, dtype=np.uint8)\n",
    "    print(\"test data shape\",test_data.shape)\n",
    "    test_data = test_data.transpose((0, 2, 3, 1))\n",
    "    test_data = test_data.transpose((0, 1, 3, 2))\n",
    "\n",
    "    test_data = test_data.astype('float32')\n",
    "    test_data = test_data / 255\n",
    "    print('Test shape:', test_data.shape)\n",
    "    print(test_data.shape[0], 'test samples')\n",
    "    \n",
    "    return test_data, test_id\n",
    "\n",
    "\n",
    "\n",
    "# Shuffle the input training data to aid Stochastic gradient descent\n",
    "def shuffle_train(X_train,y_train):\n",
    "    num_recs_train = X_train.shape[0]\n",
    "    indices = np.arange(num_recs_train)\n",
    "    np.random.shuffle(indices)\n",
    "    return X_train[indices],y_train[indices]\n",
    "\n",
    "def train(X_train, y_train, train_id,lr=0.01,epochs=200,batch_size=128, \\\n",
    "          n_classes=3,display_step=1,in_channels=3,filter_size=3,strides=1, maxpool_ksize=2,\\\n",
    "          filters=[64, 128,256],fc_units=[512,512],dropout=0.5):\n",
    "\n",
    "    model = conv_model(filters=filters, fc_units=fc_units, kernel_size=filter_size, \\\n",
    "                       strides=strides, padding='SAME', ksize=maxpool_ksize, n_classes=n_classes,dropout=dropout)\n",
    "    \n",
    "    model_graph = tf.function(model)\n",
    "\n",
    "    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.SUM)\n",
    "    optimizer = tf.keras.optimizers.Adam(lr)\n",
    "\n",
    "    num_train_recs, num_test_recs = X_train.shape[0], X_test.shape[0]\n",
    "    num_batches = num_train_recs // batch_size\n",
    "    loss_trace , accuracy_trace = [],[]\n",
    "    start_time = time.time()\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        loss, accuracy = 0, 0\n",
    "        X_train, y_train = shuffle_train(X_train,y_train)\n",
    "        \n",
    "        for j in range(num_batches):\n",
    "            X_train_batch = tf.constant(X_train[j * batch_size:(j + 1) * batch_size], dtype=tf.float32)\n",
    "            y_train_batch = tf.constant(y_train[j * batch_size:(j + 1) * batch_size])\n",
    "            \n",
    "            with tf.GradientTape() as tape:\n",
    "                y_pred_batch,_ = model_graph(X_train_batch,training=True)\n",
    "                loss_ = loss_fn(y_train_batch, y_pred_batch)\n",
    "    \n",
    "            # compute gradient\n",
    "            gradients = tape.gradient(loss_, model.trainable_variables)\n",
    "            # update the parameters\n",
    "            optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "    \n",
    "            accuracy += np.sum(y_train_batch.numpy() == np.argmax(y_pred_batch.numpy(), axis=1))\n",
    "            loss += loss_.numpy()\n",
    "        loss /= num_train_recs\n",
    "        accuracy /= num_train_recs\n",
    "        loss_trace.append(loss)\n",
    "        accuracy_trace.append(accuracy)\n",
    "        print(f\"----------------------------------------------------------------------------\\n\")\n",
    "        print(f\"Epoch {i + 1} : loss: {np.round(loss, 4)} ,accuracy:{np.round(accuracy, 4)}\\n\")\n",
    "        print(f\"----------------------------------------------------------------------------\\n\")\n",
    "\n",
    "    return model_graph\n",
    "\n",
    "def prediction(model,X_test,test_id,batch_size=32):\n",
    "    batches = X_test.shape[0]//batch_size\n",
    "    probs_array = []\n",
    "    for i in range(batches):\n",
    "        X_batch = tf.constant(X_test[(i+1)*batch_size: i*batch_size])\n",
    "        _,probs = model(X_batch,training=False)\n",
    "        probs = list(probs.numpy().tolist())\n",
    "        probs_array += probs\n",
    "    probs_array = np.array(probs_array)\n",
    "\n",
    "\n",
    "def main(train_path, test_path,input_size=64,  \\\n",
    "          lr=0.01,epochs=200,batch_size=128,\\\n",
    "          n_classes=3,display_step=1,in_channels=3,\\\n",
    "          filter_size=3,strides=1, maxpool_ksize=2,\\\n",
    "          filters=[64, 128,256],fc_units=[512,512],dropout=0.5):\n",
    "    \n",
    "    with ElapsedTimer(f'Training data processing..\\n'):\n",
    "        X_train, y_train, train_id = read_and_normalize_train_data(train_path,input_size)\n",
    "    \n",
    "    with ElapsedTimer(f'Test data processing..\\n'):\n",
    "        X_test, test_id = read_and_normalize_test_data(test_path, input_size)\n",
    "    \n",
    "    with ElapsedTimer(\"Training...\\n\"):\n",
    "        model = train(X_train, y_train, train_id,\\\n",
    "          lr=lr, epochs=epochs, batch_size=batch_size,\\\n",
    "          n_classes=n_classes, display_step=1, in_channels=in_channels,\\\n",
    "          filter_size=filter_size, strides=strides, maxpool_ksize=maxpool_ksize,\\\n",
    "          filters=filters, fc_units=fc_units, dropout=dropout)\n",
    "        \n",
    "    with ElapsedTimer(f\"Predictions on test data..\"):\n",
    "        out = prediction(model,X_test,test_id,batch_size=32)\n",
    "        df = pd.DataFrame(out, columns=['Type_1','Type_2','Type_3'])\n",
    "        df['image_name'] = test_id\n",
    "        df.to_csv('results.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convert to numpy...\n",
      "Reshape...\n",
      "Convert to float...\n",
      "Train shape: (8206, 64, 64, 3)\n",
      "8206 train samples\n",
      "20.370 min: Training data processing..\n",
      "\n",
      "test data shape (4018, 64, 64, 3)\n",
      "Test shape: (4018, 64, 64, 3)\n",
      "4018 test samples\n",
      "7.023 min: Test data processing..\n",
      "\n",
      "WARNING:tensorflow:AutoGraph could not transform <__main__.conv_model object at 0x7fe62c419ac0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <__main__.conv_model object at 0x7fe62c419ac0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <bound method conv_model.call of <__main__.conv_model object at 0x7fe62c419ac0>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <bound method conv_model.call of <__main__.conv_model object at 0x7fe62c419ac0>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 1 : loss: 6.8441 ,accuracy:0.5146\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 2 : loss: 1.0041 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 3 : loss: 1.0024 ,accuracy:0.5294\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 4 : loss: 1.0024 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 5 : loss: 1.0021 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 6 : loss: 1.0013 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 7 : loss: 1.0008 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 8 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 9 : loss: 1.002 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 10 : loss: 1.0015 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 11 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 12 : loss: 1.0006 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 13 : loss: 1.0009 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 14 : loss: 1.0007 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 15 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 16 : loss: 1.0005 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 17 : loss: 1.0007 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 18 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 19 : loss: 1.0008 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 20 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 21 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 22 : loss: 1.0009 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 23 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 24 : loss: 1.0001 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 25 : loss: 1.0007 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 26 : loss: 1.0008 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 27 : loss: 1.0003 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 28 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 29 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 30 : loss: 1.0009 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 31 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 32 : loss: 1.0007 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 33 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 34 : loss: 1.0011 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 35 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 36 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 37 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 38 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 39 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 40 : loss: 1.0002 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 41 : loss: 0.9999 ,accuracy:0.5291\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 42 : loss: 1.0006 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 43 : loss: 1.0003 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 44 : loss: 1.0009 ,accuracy:0.5282\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 45 : loss: 1.0003 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 46 : loss: 1.0002 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 47 : loss: 1.0006 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 48 : loss: 1.0008 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 49 : loss: 1.0003 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 50 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 51 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 52 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 53 : loss: 1.0011 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 54 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 55 : loss: 1.0002 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 56 : loss: 1.0003 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 57 : loss: 1.0002 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 58 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 59 : loss: 1.0007 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 60 : loss: 1.0009 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 61 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 62 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 63 : loss: 1.0002 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 64 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 65 : loss: 1.0008 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 66 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 67 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 68 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 69 : loss: 1.0007 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 70 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 71 : loss: 1.0004 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 72 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 73 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 74 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 75 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 76 : loss: 1.0009 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 77 : loss: 1.0005 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 78 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 79 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 80 : loss: 1.0009 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 81 : loss: 1.0006 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 82 : loss: 1.0005 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 83 : loss: 1.0005 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 84 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 85 : loss: 1.0008 ,accuracy:0.528\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 86 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 87 : loss: 1.0009 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 88 : loss: 1.0001 ,accuracy:0.5291\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 89 : loss: 1.0 ,accuracy:0.5291\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 90 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 91 : loss: 1.0002 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 92 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 93 : loss: 1.0006 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 94 : loss: 1.0002 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 95 : loss: 1.0005 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 96 : loss: 1.0003 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 97 : loss: 1.0008 ,accuracy:0.5282\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 98 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 99 : loss: 1.0007 ,accuracy:0.5282\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 100 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 101 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 102 : loss: 1.0005 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 103 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 104 : loss: 1.0004 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 105 : loss: 0.9998 ,accuracy:0.5292\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 106 : loss: 1.0005 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 107 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 108 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 109 : loss: 1.0001 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 110 : loss: 1.0006 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 111 : loss: 1.0008 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 112 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 113 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 114 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 115 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 116 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 117 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 118 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 119 : loss: 1.0004 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 120 : loss: 1.0004 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 121 : loss: 1.0002 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 122 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 123 : loss: 1.0008 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 124 : loss: 1.0 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 125 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 126 : loss: 1.0003 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 127 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 128 : loss: 1.0007 ,accuracy:0.5282\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 129 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 130 : loss: 1.0008 ,accuracy:0.5282\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 131 : loss: 1.0006 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 132 : loss: 1.0001 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 133 : loss: 1.0002 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 134 : loss: 1.0003 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 135 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 136 : loss: 1.0002 ,accuracy:0.5291\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 137 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 138 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 139 : loss: 1.0005 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 140 : loss: 1.0001 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 141 : loss: 1.0004 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 142 : loss: 1.0001 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 143 : loss: 1.0009 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 144 : loss: 1.0007 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 145 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 146 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 147 : loss: 1.0003 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 148 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 149 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 150 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 151 : loss: 1.0003 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 152 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 153 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 154 : loss: 1.0006 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 155 : loss: 1.0005 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 156 : loss: 1.0003 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 157 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 158 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 159 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 160 : loss: 1.0006 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 161 : loss: 1.0007 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 162 : loss: 1.0004 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 163 : loss: 1.0002 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 164 : loss: 1.0002 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 165 : loss: 1.0004 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 166 : loss: 1.0009 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 167 : loss: 1.0002 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 168 : loss: 1.0003 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 169 : loss: 1.0006 ,accuracy:0.5288\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 170 : loss: 1.0009 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 171 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 172 : loss: 1.001 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 173 : loss: 1.0002 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 174 : loss: 1.0005 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 175 : loss: 1.0002 ,accuracy:0.529\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 176 : loss: 1.0005 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 177 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 178 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 179 : loss: 1.0009 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 180 : loss: 1.0002 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 181 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 182 : loss: 1.0007 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 183 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 184 : loss: 1.0004 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 185 : loss: 1.0003 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 186 : loss: 1.0009 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 187 : loss: 1.0007 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 188 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 189 : loss: 1.0004 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 190 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 191 : loss: 1.0007 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 192 : loss: 1.0002 ,accuracy:0.5289\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 193 : loss: 1.0001 ,accuracy:0.5292\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 194 : loss: 1.0003 ,accuracy:0.5285\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 195 : loss: 1.0007 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 196 : loss: 1.0005 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 197 : loss: 1.0006 ,accuracy:0.5284\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 198 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 199 : loss: 1.0006 ,accuracy:0.5286\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 200 : loss: 1.0007 ,accuracy:0.5283\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "9.727 min: Training...\n",
      "\n",
      "84.906 ms: Predictions on test data..\n"
     ]
    }
   ],
   "source": [
    "main(train_path='/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/Kaggle Competitions/Intel',\n",
    "    test_path='/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/Kaggle Competitions/Intel',epochs=200)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-8 Transfer Learning with Pre-trained VGG16 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version 2.4.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from pathlib import Path\n",
    "from glob import glob\n",
    "\n",
    "print('tensorflow version', tf.__version__)\n",
    "import numpy as np\n",
    "from tensorflow.keras import Model, layers\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from elapsedtimer import ElapsedTimer\n",
    "import pandas as pd\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "from tensorflow.keras import layers, Model\n",
    "\n",
    "MEAN_VALUE = np.array([103.939, 116.779, 123.68])\n",
    "\n",
    "\"\"\"\n",
    "Model class \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "class vgg16_customized(Model):\n",
    "\n",
    "    def __init__(self,activation='relu', n_classes=2, input_size=224):\n",
    "        super(vgg16_customized,self).__init__()\n",
    "        self.base_model = VGG16(weights='imagenet', include_top=False, input_shape=(input_size, input_size, 3))\n",
    "        self.base_model.trainable = False\n",
    "        self.flatten = layers.Flatten()\n",
    "        self.out = layers.Dense(n_classes-1)\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.base_model(x)\n",
    "        x = self.flatten(x)\n",
    "        #x = self.fc1(x)\n",
    "        #x = self.fc2(x)\n",
    "        out = self.out(x)\n",
    "        probs = tf.nn.sigmoid(out)\n",
    "        return out, probs\n",
    "\n",
    "\n",
    "\n",
    "# Routine to read the Images and also do mean correction\n",
    "def image_preprocess(img_path, width, height):\n",
    "    img = cv2.imread(img_path)\n",
    "    img = cv2.resize(img, (width, height))\n",
    "    img = img - MEAN_VALUE\n",
    "    return img\n",
    "\n",
    "\n",
    "# Create generator for Image batches so that only the processed batch is in memory\n",
    "def data_gen_batch(images, batch_size, width, height):\n",
    "    \"\"\"\n",
    "    data_dir: where the actual images are kept\n",
    "    mask_dir: where the actual masks are kept\n",
    "    images: the filenames of the images we want to generate batches from\n",
    "    batch_size: \n",
    "    dims: the dimensions in which we want to rescale our images\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        ix = np.random.choice(np.arange(len(images)), batch_size)\n",
    "        imgs = []\n",
    "        labels = []\n",
    "        for i in ix:\n",
    "            \n",
    "            if images[i].split('/')[-1].split('.')[0] == 'cat':\n",
    "                labels.append(1)\n",
    "                \n",
    "            else:\n",
    "                if images[i].split('/')[-1].split('.')[0] == 'dog':\n",
    "                    labels.append(0)\n",
    "                    \n",
    "            img_path = f\"{Path(images[i])}\"\n",
    "            array_img = image_preprocess(img_path, width, height)\n",
    "            imgs.append(array_img)\n",
    "\n",
    "        imgs = np.array(imgs)\n",
    "        labels = np.array(labels)\n",
    "        labels = np.reshape(labels, (batch_size, 1))\n",
    "        yield imgs, labels\n",
    "\n",
    "\n",
    "def train(train_data_dir, lr=0.01, input_size=224,n_classes=2, batch_size=32, output_dir=None,epochs=10):\n",
    "    if output_dir is None:\n",
    "        output_dir = os.getcwd()\n",
    "    else:\n",
    "        if not Path(output_dir).exists():\n",
    "            os.makedirs(output_dir)\n",
    "\n",
    "    checkpoints_dir = f'{output_dir}/checkpoints_dir'\n",
    "    os.makedirs(checkpoints_dir, exist_ok=True)\n",
    "\n",
    "    all_images = glob(f\"{train_data_dir}/*/*\")\n",
    "    print(len(all_images))\n",
    "    train_images, val_images = train_test_split(all_images, train_size=0.8, test_size=0.2)\n",
    "    train_images = train_images\n",
    "    val_images = val_images\n",
    "    \n",
    "    print(f\"Number of training images: {len(train_images)}\")\n",
    "    print(f\"Number of validation images: {len(val_images)}\")\n",
    "\n",
    "    train_gen = data_gen_batch(train_images, batch_size, 224, 224)\n",
    "    val_gen = data_gen_batch(val_images, batch_size, 224, 224)\n",
    "    \n",
    "    model = vgg16_customized(activation='relu', n_classes=n_classes, input_size=input_size)\n",
    "    model_graph = tf.function(model)\n",
    "    \n",
    "    loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.SUM)\n",
    "    #loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "    optimizer = tf.keras.optimizers.Adam(lr)\n",
    "\n",
    "    batches = len(train_images)//batch_size\n",
    "    batches_val = len(val_images) // batch_size\n",
    "    loss_trace, accuracy_trace = [], []\n",
    "    for epoch in range(epochs):\n",
    "        loss, accuracy = 0, 0\n",
    "        num_train_recs = 0\n",
    "        for batch in range(batches):\n",
    "            x_train_batch, y_train_batch = next(train_gen)\n",
    "            x_train_batch, y_train_batch = tf.constant(x_train_batch), tf.constant(y_train_batch)\n",
    "            num_train_recs += x_train_batch.shape[0] \n",
    "            with tf.GradientTape() as tape:\n",
    "                y_pred_batch,y_pred_probs = model_graph(x_train_batch, training=True)\n",
    "                loss_ = loss_fn(y_train_batch, y_pred_batch)\n",
    "\n",
    "            # compute gradient\n",
    "            gradients = tape.gradient(loss_, model.trainable_variables)\n",
    "            # update the parameters\n",
    "            optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "            y_pred_probs, y_train_batch = y_pred_probs.numpy() , y_train_batch.numpy()\n",
    "            y_pred_probs[y_pred_probs >= 0.5] = 1.0\n",
    "            accuracy += np.sum(y_train_batch == y_pred_probs)\n",
    "            loss += loss_.numpy()\n",
    "            #print(f\"Loss for Epoch {epoch} Batch {batch}: {loss_.numpy()}\")\n",
    "        loss /= num_train_recs\n",
    "        accuracy /= num_train_recs\n",
    "        loss_trace.append(loss)\n",
    "        accuracy_trace.append(accuracy)\n",
    "        print(f\"----------------------------------------------------------------------------\\n\")\n",
    "        print(f\"Epoch {epoch} : loss: {np.round(loss, 4)} ,accuracy:{np.round(accuracy, 4)}\\n\")\n",
    "        print(f\"----------------------------------------------------------------------------\\n\")\n",
    "    \n",
    "    accuracy_val, num_val_recs = 0, 0\n",
    "    for batch in range(batches_val):\n",
    "        X_val_batch, y_val_batch = next(val_gen)\n",
    "        X_val_batch = tf.constant(X_val_batch)\n",
    "        num_val_recs += X_val_batch.shape[0] \n",
    "        _,y_probs = model_graph(X_val_batch,training=False)\n",
    "        y_probs = y_probs.numpy()\n",
    "        y_probs[y_probs >= 0.5] = 1.0\n",
    "        #print(y_probs.shape,y_val_batch.shape)\n",
    "        accuracy_val += np.sum(y_val_batch == y_probs)\n",
    "    accuracy_val /= num_val_recs\n",
    "    print(f\"Validation Accuracy at Epoch {epoch}: {accuracy_val}\")\n",
    "    return model_graph, X_val_batch, y_val_batch, y_probs\n",
    "    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000\n",
      "Number of training images: 20000\n",
      "Number of validation images: 5000\n",
      "WARNING:tensorflow:AutoGraph could not transform <__main__.vgg16_customized object at 0x7fe42a02d520> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <__main__.vgg16_customized object at 0x7fe42a02d520> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Epoch 0 : loss: 5.692 ,accuracy:0.9669\n",
      "\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Validation Accuracy at Epoch 0: 0.9769631410256411\n"
     ]
    }
   ],
   "source": [
    "model_graph, X_val_batch, y_val_batch, y_probs = train(train_data_dir='/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/CatvsDog/train',batch_size=32,epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class: Dog Predicted Class: Dog\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict ={1:\"Cat\",0:\"Dog\"}\n",
    "plt.imshow((X_val_batch[0,:] + MEAN_VALUE)/255.0)\n",
    "print(\"Actual class:\",class_dict[y_val_batch[0][0]],\"Predicted Class:\",class_dict[y_probs[0][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class: Cat Predicted Class: Cat\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict ={1:\"Cat\",0:\"Dog\"}\n",
    "plt.imshow((X_val_batch[3,:] + MEAN_VALUE)/255.0)\n",
    "print(\"Actual class:\",class_dict[y_val_batch[3][0]],\"Predicted Class:\",class_dict[y_probs[3][0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-9a DepthWise Separable Convolution vs Standard Convolution \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, SeparableConv2D, MaxPooling2D\n",
    "\n",
    "\n",
    "# Conv Model definition\n",
    "def conv_model(kernel_size=3, pool_ksize=2, num_filters=[32, 64], hidden_units=[256], activation='relu',\n",
    "               input_shape=(28, 28, 1), n_classes=10, conv_type='standard', dropout=0.25):\n",
    "    # Create the model\n",
    "    model = Sequential()\n",
    "    if conv_type == 'depth_wise':\n",
    "        model.add(SeparableConv2D(num_filters[0], kernel_size=(kernel_size, kernel_size), activation=activation,\n",
    "                                  input_shape=input_shape))\n",
    "    else:\n",
    "\n",
    "        model.add(Conv2D(num_filters[0], kernel_size=(kernel_size, kernel_size), activation=activation,\n",
    "                         input_shape=input_shape))\n",
    "\n",
    "    model.add(MaxPooling2D(pool_size=(pool_ksize, pool_ksize)))\n",
    "    model.add(Dropout(dropout))\n",
    "\n",
    "    if conv_type == 'depth_wise':\n",
    "        model.add(SeparableConv2D(num_filters[1], kernel_size=(kernel_size, kernel_size), activation=activation))\n",
    "    else:\n",
    "        model.add(Conv2D(num_filters[1], kernel_size=(kernel_size, kernel_size), activation=activation))\n",
    "\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(hidden_units[0], activation=activation))\n",
    "    model.add(Dense(n_classes, activation='softmax'))\n",
    "    return model\n",
    "\n",
    "\n",
    "def train(input_size=28, input_channels=1, epochs=10, n_classes=10,\n",
    "          val_ratio=0.3, kernel_size=3, pool_ksize=2, num_filters=[32, 64],\n",
    "          hidden_units=[256], activation='relu',\n",
    "          dropout=0.25, lr=0.01, batch_size=128, conv_type='standard'):\n",
    "    print(f\"Loading MNIST dataset and formatting..\\n\")\n",
    "    (input_train, target_train), (input_test, target_test) = mnist.load_data()\n",
    "\n",
    "    # Reshape the data\n",
    "    input_train = input_train.reshape(input_train.shape[0], input_size, input_size, 1)\n",
    "    input_test = input_test.reshape(input_test.shape[0], input_size, input_size, 1)\n",
    "    input_shape = (input_size, input_size, input_channels)\n",
    "\n",
    "    input_train = input_train.astype('float32')\n",
    "    input_test = input_test.astype('float32')\n",
    "\n",
    "    print(f\"Normalize the input..\\n\")\n",
    "    input_train = input_train / 255\n",
    "    input_test = input_test / 255\n",
    "\n",
    "    # Convert target vectors to categorical targets\n",
    "    target_train = tensorflow.keras.utils.to_categorical(target_train, n_classes)\n",
    "    target_test = tensorflow.keras.utils.to_categorical(target_test, n_classes)\n",
    "\n",
    "    print(f\"Define Model..\\n\")\n",
    "    model = conv_model(kernel_size=kernel_size, pool_ksize=pool_ksize, num_filters=num_filters,\n",
    "                       hidden_units=hidden_units, activation=activation, input_shape=input_shape,\n",
    "                       n_classes=n_classes, conv_type=conv_type, dropout=dropout)\n",
    "    print(model)\n",
    "    # Compile the model\n",
    "    print(f\"Compile Model..\\n\")\n",
    "    model.compile(loss=tensorflow.keras.losses.categorical_crossentropy,\n",
    "                  optimizer=tensorflow.keras.optimizers.Adam(),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    print(f\"Train Model..\\n\")\n",
    "    model.fit(input_train, target_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              verbose=1,\n",
    "              validation_split=val_ratio)\n",
    "\n",
    "    print(f\"Evaluation test data..\\n\")\n",
    "    score = model.evaluate(input_test, target_test, verbose=0)\n",
    "    print(f'Test loss: {score[0]} / Test accuracy: {score[1]}')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-9b Run Standard Convolution \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading MNIST dataset and formatting..\n",
      "\n",
      "Normalize the input..\n",
      "\n",
      "Define Model..\n",
      "\n",
      "<tensorflow.python.keras.engine.sequential.Sequential object at 0x7fe6cdcce400>\n",
      "Compile Model..\n",
      "\n",
      "Train Model..\n",
      "\n",
      "Epoch 1/10\n",
      "329/329 [==============================] - 3s 6ms/step - loss: 0.5855 - accuracy: 0.8199 - val_loss: 0.0798 - val_accuracy: 0.9764\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0945 - accuracy: 0.9704 - val_loss: 0.0648 - val_accuracy: 0.9791\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0618 - accuracy: 0.9812 - val_loss: 0.0632 - val_accuracy: 0.9793\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0496 - accuracy: 0.9847 - val_loss: 0.0445 - val_accuracy: 0.9858\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0374 - accuracy: 0.9880 - val_loss: 0.0435 - val_accuracy: 0.9863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0292 - accuracy: 0.9905 - val_loss: 0.0395 - val_accuracy: 0.9878\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0272 - accuracy: 0.9908 - val_loss: 0.0392 - val_accuracy: 0.9885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/10\n",
      "329/329 [==============================] - 2s 5ms/step - loss: 0.0261 - accuracy: 0.9913 - val_loss: 0.0370 - val_accuracy: 0.9894\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/10\n",
      "329/329 [==============================] - 1s 4ms/step - loss: 0.0207 - accuracy: 0.9933 - val_loss: 0.0329 - val_accuracy: 0.9894\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/10\n",
      "329/329 [==============================] - 1s 4ms/step - loss: 0.0174 - accuracy: 0.9946 - val_loss: 0.0367 - val_accuracy: 0.9898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Evaluation test data..\n",
      "\n",
      "Test loss: 0.028370419517159462 / Test accuracy: 0.9904999732971191\n"
     ]
    }
   ],
   "source": [
    "train(conv_type='standard')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listing 3-9b Run DepthWise Separable Convolution \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading MNIST dataset and formatting..\n",
      "\n",
      "Normalize the input..\n",
      "\n",
      "Define Model..\n",
      "\n",
      "<tensorflow.python.keras.engine.sequential.Sequential object at 0x7fe6380a0d60>\n",
      "Compile Model..\n",
      "\n",
      "Train Model..\n",
      "\n",
      "Epoch 1/10\n",
      "329/329 [==============================] - 3s 7ms/step - loss: 0.9277 - accuracy: 0.7350 - val_loss: 0.1852 - val_accuracy: 0.9440\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.1840 - accuracy: 0.9433 - val_loss: 0.1183 - val_accuracy: 0.9641\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.1351 - accuracy: 0.9567 - val_loss: 0.1044 - val_accuracy: 0.9680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.1063 - accuracy: 0.9661 - val_loss: 0.0912 - val_accuracy: 0.9709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.0926 - accuracy: 0.9701 - val_loss: 0.0745 - val_accuracy: 0.9777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.0757 - accuracy: 0.9756 - val_loss: 0.0695 - val_accuracy: 0.9785\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/10\n",
      "329/329 [==============================] - 2s 7ms/step - loss: 0.0656 - accuracy: 0.9783 - val_loss: 0.0628 - val_accuracy: 0.9809\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.0607 - accuracy: 0.9813 - val_loss: 0.0594 - val_accuracy: 0.9820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.0531 - accuracy: 0.9827 - val_loss: 0.0574 - val_accuracy: 0.9825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/10\n",
      "329/329 [==============================] - 2s 6ms/step - loss: 0.0505 - accuracy: 0.9838 - val_loss: 0.0547 - val_accuracy: 0.9841\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Evaluation test data..\n",
      "\n",
      "Test loss: 0.04192721098661423 / Test accuracy: 0.9858999848365784\n"
     ]
    }
   ],
   "source": [
    "train(conv_type='depth_wise')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
